{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Borrowing Capacity\n",
    "\n",
    "This notebook calculates the book borrowing capacity for members of the S&Co library.  Since library memberships defined a limit on the amount of books a person could borrow at a time (typically only one), we define the borrowing capacity of the library as the total number of books that could be checked out at any given time based on the active subscriptions.\n",
    "\n",
    "This code helps us generate figure 7 for our paper."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XrN8dF0AbDXw"
   },
   "source": [
    "## Load Libraries & Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5ZN07dAW8Nxa",
    "outputId": "6344d274-6bd2-4c19-8118-e3c3f8f4b86f"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import sys\n",
    "sys.path.append('..')\n",
    "\n",
    "from utils.missing_data_processing import *\n",
    "events_df, members_df, books_df, _  = load_initial_data()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZAiyausmbGw0"
   },
   "source": [
    "## limit to events with complete start and end dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 525
    },
    "id": "lvWAnA1T8Z8C",
    "outputId": "bd4e7ccd-afec-4c49-c912-bf74ec9c7381"
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>event_type</th>\n",
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       "      <th>subscription_duration_days</th>\n",
       "      <th>...</th>\n",
       "      <th>item_volume</th>\n",
       "      <th>item_authors</th>\n",
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       "      <th>item_notes</th>\n",
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       "      <td>Claude Cahun / Mlle Lucie Schwob</td>\n",
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       "      <td>https://iiif.princeton.edu/loris/figgy_prod/3a...</td>\n",
       "      <td>1919-11-17</td>\n",
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       "      <td>1920-11-17</td>\n",
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       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>366.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Logbook</td>\n",
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       "      <th>29914</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1919-11-17</td>\n",
       "      <td>1920-02-17</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Mrs. Worthing</td>\n",
       "      <td>Worthing, Mrs.</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3 months</td>\n",
       "      <td>92.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Logbook</td>\n",
       "      <td>Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1919-11-17</td>\n",
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       "      <th>674</th>\n",
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       "      <td>1919-11-18</td>\n",
       "      <td>1919-11-28</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Denise Ulmann</td>\n",
       "      <td>Ulmann, Denise</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Wilde, Oscar</td>\n",
       "      <td>1905.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lending Library Card</td>\n",
       "      <td>Sylvia Beach, Mlle Denise Ulmann Lending Libra...</td>\n",
       "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
       "      <td>https://iiif.princeton.edu/loris/figgy_prod/86...</td>\n",
       "      <td>1919-11-18</td>\n",
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       "      <th>29916</th>\n",
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       "      <td>1920-05-18</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Anne Evans</td>\n",
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       "      <td>35.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6 months</td>\n",
       "      <td>182.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Logbook</td>\n",
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      "text/plain": [
       "         event_type  start_date    end_date  \\\n",
       "671    Subscription  1919-11-17  1919-12-17   \n",
       "29915  Subscription  1919-11-17  1920-11-17   \n",
       "29914  Subscription  1919-11-17  1920-02-17   \n",
       "674          Borrow  1919-11-18  1919-11-28   \n",
       "29916  Subscription  1919-11-18  1920-05-18   \n",
       "\n",
       "                                             member_uris  \\\n",
       "671    https://shakespeareandco.princeton.edu/members...   \n",
       "29915  https://shakespeareandco.princeton.edu/members...   \n",
       "29914  https://shakespeareandco.princeton.edu/members...   \n",
       "674    https://shakespeareandco.princeton.edu/members...   \n",
       "29916  https://shakespeareandco.princeton.edu/members...   \n",
       "\n",
       "                           member_names member_sort_names  \\\n",
       "671    Claude Cahun / Mlle Lucie Schwob     Cahun, Claude   \n",
       "29915                     Marcelle Flot    Flot, Marcelle   \n",
       "29914                     Mrs. Worthing    Worthing, Mrs.   \n",
       "674                       Denise Ulmann    Ulmann, Denise   \n",
       "29916                        Anne Evans       Evans, Anne   \n",
       "\n",
       "       subscription_price_paid  subscription_deposit subscription_duration  \\\n",
       "671                        4.0                   NaN               1 month   \n",
       "29915                     28.0                   NaN                1 year   \n",
       "29914                     12.0                   NaN              3 months   \n",
       "674                        NaN                   NaN                   NaN   \n",
       "29916                     35.0                  14.0              6 months   \n",
       "\n",
       "       subscription_duration_days  ...  item_volume  item_authors item_year  \\\n",
       "671                          30.0  ...          NaN           NaN       NaN   \n",
       "29915                       366.0  ...          NaN           NaN       NaN   \n",
       "29914                        92.0  ...          NaN           NaN       NaN   \n",
       "674                           NaN  ...          NaN  Wilde, Oscar    1905.0   \n",
       "29916                       182.0  ...          NaN           NaN       NaN   \n",
       "\n",
       "       item_notes                   source_type  \\\n",
       "671           NaN  Lending Library Card;Logbook   \n",
       "29915         NaN                       Logbook   \n",
       "29914         NaN                       Logbook   \n",
       "674           NaN          Lending Library Card   \n",
       "29916         NaN                       Logbook   \n",
       "\n",
       "                                         source_citation  \\\n",
       "671    Sylvia Beach, Lucie Schwob Lending Library Car...   \n",
       "29915  Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...   \n",
       "29914  Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...   \n",
       "674    Sylvia Beach, Mlle Denise Ulmann Lending Libra...   \n",
       "29916  Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...   \n",
       "\n",
       "                                         source_manifest  \\\n",
       "671    https://figgy.princeton.edu/concern/scanned_re...   \n",
       "29915                                                NaN   \n",
       "29914                                                NaN   \n",
       "674    https://figgy.princeton.edu/concern/scanned_re...   \n",
       "29916                                                NaN   \n",
       "\n",
       "                                            source_image start_datetime  \\\n",
       "671    https://iiif.princeton.edu/loris/figgy_prod/3a...     1919-11-17   \n",
       "29915                                                NaN     1919-11-17   \n",
       "29914                                                NaN     1919-11-17   \n",
       "674    https://iiif.princeton.edu/loris/figgy_prod/86...     1919-11-18   \n",
       "29916                                                NaN     1919-11-18   \n",
       "\n",
       "      end_datetime  \n",
       "671     1919-12-17  \n",
       "29915   1920-11-17  \n",
       "29914   1920-02-17  \n",
       "674     1919-11-28  \n",
       "29916   1920-05-18  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subset data for only those with complete start and end dates\n",
    "date_events = events_df[(events_df.start_date.str.len() > 9) & (events_df.end_date.str.len() > 9)].copy()\n",
    "# turn start/end dates into datetimes\n",
    "date_events['start_datetime'] = pd.to_datetime(date_events.start_date, format='%Y-%m-%d', errors='ignore')\n",
    "date_events['end_datetime'] = pd.to_datetime(date_events.end_date, format='%Y-%m-%d', errors='ignore')\n",
    "date_events = date_events.sort_values(by=['start_datetime'])\n",
    "date_events.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uJUfL88HEDRd",
    "outputId": "64ce0f72-5808-45a4-faf7-60337590ae8e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('1919-11-17 00:00:00')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earliest_date = date_events.start_datetime.iloc[0]\n",
    "earliest_date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bblWV_zPbMg4"
   },
   "source": [
    "## separate out borrow events and subscription events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "Ubr65IM6AFMb"
   },
   "outputs": [],
   "source": [
    "# borrow events \n",
    "borrow_events = date_events[date_events.event_type == 'Borrow']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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    "id": "wkfYnAXJ_9cl",
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       "      <td>Sylvia Beach, Lucie Schwob Lending Library Car...</td>\n",
       "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
       "      <td>https://iiif.princeton.edu/loris/figgy_prod/3a...</td>\n",
       "      <td>1919-11-17</td>\n",
       "      <td>1919-12-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29915</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1919-11-17</td>\n",
       "      <td>1920-11-17</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Marcelle Flot</td>\n",
       "      <td>Flot, Marcelle</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1 year</td>\n",
       "      <td>366.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Logbook</td>\n",
       "      <td>Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1919-11-17</td>\n",
       "      <td>1920-11-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29914</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1919-11-17</td>\n",
       "      <td>1920-02-17</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Mrs. Worthing</td>\n",
       "      <td>Worthing, Mrs.</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3 months</td>\n",
       "      <td>92.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Logbook</td>\n",
       "      <td>Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1919-11-17</td>\n",
       "      <td>1920-02-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29916</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1919-11-18</td>\n",
       "      <td>1920-05-18</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Anne Evans</td>\n",
       "      <td>Evans, Anne</td>\n",
       "      <td>35.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6 months</td>\n",
       "      <td>182.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Logbook</td>\n",
       "      <td>Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1919-11-18</td>\n",
       "      <td>1920-05-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1919-11-18</td>\n",
       "      <td>1919-12-18</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Henri Regnier</td>\n",
       "      <td>Regnier, Henri</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1 month</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Logbook;Lending Library Card</td>\n",
       "      <td>Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...</td>\n",
       "      <td>;https://figgy.princeton.edu/concern/scanned_r...</td>\n",
       "      <td>;https://iiif.princeton.edu/loris/figgy_prod/c...</td>\n",
       "      <td>1919-11-18</td>\n",
       "      <td>1919-12-18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-975b6294-c01d-4e1f-a4e6-568bb9228934')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
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       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "         event_type  start_date    end_date  \\\n",
       "671    Subscription  1919-11-17  1919-12-17   \n",
       "29915  Subscription  1919-11-17  1920-11-17   \n",
       "29914  Subscription  1919-11-17  1920-02-17   \n",
       "29916  Subscription  1919-11-18  1920-05-18   \n",
       "672    Subscription  1919-11-18  1919-12-18   \n",
       "\n",
       "                                             member_uris  \\\n",
       "671    https://shakespeareandco.princeton.edu/members...   \n",
       "29915  https://shakespeareandco.princeton.edu/members...   \n",
       "29914  https://shakespeareandco.princeton.edu/members...   \n",
       "29916  https://shakespeareandco.princeton.edu/members...   \n",
       "672    https://shakespeareandco.princeton.edu/members...   \n",
       "\n",
       "                           member_names member_sort_names  \\\n",
       "671    Claude Cahun / Mlle Lucie Schwob     Cahun, Claude   \n",
       "29915                     Marcelle Flot    Flot, Marcelle   \n",
       "29914                     Mrs. Worthing    Worthing, Mrs.   \n",
       "29916                        Anne Evans       Evans, Anne   \n",
       "672                       Henri Regnier    Regnier, Henri   \n",
       "\n",
       "       subscription_price_paid  subscription_deposit subscription_duration  \\\n",
       "671                        4.0                   NaN               1 month   \n",
       "29915                     28.0                   NaN                1 year   \n",
       "29914                     12.0                   NaN              3 months   \n",
       "29916                     35.0                  14.0              6 months   \n",
       "672                        4.0                   5.6               1 month   \n",
       "\n",
       "       subscription_duration_days  ...  item_volume item_authors item_year  \\\n",
       "671                          30.0  ...          NaN          NaN       NaN   \n",
       "29915                       366.0  ...          NaN          NaN       NaN   \n",
       "29914                        92.0  ...          NaN          NaN       NaN   \n",
       "29916                       182.0  ...          NaN          NaN       NaN   \n",
       "672                          30.0  ...          NaN          NaN       NaN   \n",
       "\n",
       "       item_notes                   source_type  \\\n",
       "671           NaN  Lending Library Card;Logbook   \n",
       "29915         NaN                       Logbook   \n",
       "29914         NaN                       Logbook   \n",
       "29916         NaN                       Logbook   \n",
       "672           NaN  Logbook;Lending Library Card   \n",
       "\n",
       "                                         source_citation  \\\n",
       "671    Sylvia Beach, Lucie Schwob Lending Library Car...   \n",
       "29915  Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...   \n",
       "29914  Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...   \n",
       "29916  Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...   \n",
       "672    Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...   \n",
       "\n",
       "                                         source_manifest  \\\n",
       "671    https://figgy.princeton.edu/concern/scanned_re...   \n",
       "29915                                                NaN   \n",
       "29914                                                NaN   \n",
       "29916                                                NaN   \n",
       "672    ;https://figgy.princeton.edu/concern/scanned_r...   \n",
       "\n",
       "                                            source_image start_datetime  \\\n",
       "671    https://iiif.princeton.edu/loris/figgy_prod/3a...     1919-11-17   \n",
       "29915                                                NaN     1919-11-17   \n",
       "29914                                                NaN     1919-11-17   \n",
       "29916                                                NaN     1919-11-18   \n",
       "672    ;https://iiif.princeton.edu/loris/figgy_prod/c...     1919-11-18   \n",
       "\n",
       "      end_datetime  \n",
       "671     1919-12-17  \n",
       "29915   1920-11-17  \n",
       "29914   1920-02-17  \n",
       "29916   1920-05-18  \n",
       "672     1919-12-18  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subset data for subscription events with complete dates\n",
    "subscription_events = date_events[date_events.event_type.isin(['Subscription', 'Renewal', 'Supplement'])]\n",
    "subscription_events.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xcF6EXY-Fxrq",
    "outputId": "0475331d-e3bf-465b-ec0f-06e0815b7a2c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('1942-09-03 00:00:00')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# end date for the last subscription\n",
    "subs_end_date = subscription_events.end_datetime.max()\n",
    "subs_end_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vOlAlkzdBg-B",
    "outputId": "f7992056-2ec4-407f-eaa8-bf80399efb40"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1919-11-17 00:00:00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day = subscription_events.start_datetime.iloc[0]\n",
    "print(day)\n",
    "subscription_events[(subscription_events.start_datetime <= day) & (day < subscription_events.end_datetime)].subscription_volumes.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "id": "_xA4JswxSzBV",
    "outputId": "d1dc1fac-c60f-48ff-a5d1-c6cf4e1904e7"
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subscription_volumes</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2524</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>32</td>\n",
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       "      <td>2</td>\n",
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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       "  "
      ],
      "text/plain": [
       "   subscription_volumes  count\n",
       "0                   1.0   6086\n",
       "1                   2.0   2524\n",
       "2                   3.0     58\n",
       "3                   4.0     32\n",
       "4                   5.0      2\n",
       "5                   6.0      2\n",
       "6                   7.0      1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subs_vol_totals = subscription_events.groupby([\"subscription_volumes\"]).size().reset_index(name='count').sort_values(['count'], ascending=False)\n",
    "# alternately, can be generated more simply via\n",
    "# subscription_events.subscription_volumes.value_counts()\n",
    "subs_vol_totals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "I_bzr-9ZTBla",
    "outputId": "5af68ff4-5287-4d3d-ded6-fc60fd4afc98"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_type</th>\n",
       "      <th>start_date</th>\n",
       "      <th>end_date</th>\n",
       "      <th>member_uris</th>\n",
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       "      <th>subscription_deposit</th>\n",
       "      <th>subscription_duration</th>\n",
       "      <th>subscription_duration_days</th>\n",
       "      <th>...</th>\n",
       "      <th>item_volume</th>\n",
       "      <th>item_authors</th>\n",
       "      <th>item_year</th>\n",
       "      <th>item_notes</th>\n",
       "      <th>source_type</th>\n",
       "      <th>source_citation</th>\n",
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       "      <th>source_image</th>\n",
       "      <th>start_datetime</th>\n",
       "      <th>end_datetime</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30096</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1920-11-13</td>\n",
       "      <td>1920-12-13</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Mrs. Pierce Williams</td>\n",
       "      <td>Williams, Mrs. Pierce</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1 month</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Logbook</td>\n",
       "      <td>Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1920-11-13</td>\n",
       "      <td>1920-12-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3640</th>\n",
       "      <td>Supplement</td>\n",
       "      <td>1923-12-27</td>\n",
       "      <td>1924-03-27</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Mme Lowenfeld</td>\n",
       "      <td>Lowenfeld, Mme</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3 months</td>\n",
       "      <td>91.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Logbook</td>\n",
       "      <td>Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1923-12-27</td>\n",
       "      <td>1924-03-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4060</th>\n",
       "      <td>Renewal</td>\n",
       "      <td>1924-05-01</td>\n",
       "      <td>1924-06-01</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Colonel Thackeray</td>\n",
       "      <td>Thackeray, Colonel</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1 month</td>\n",
       "      <td>31.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Logbook</td>\n",
       "      <td>Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1924-05-01</td>\n",
       "      <td>1924-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31329</th>\n",
       "      <td>Renewal</td>\n",
       "      <td>1924-06-10</td>\n",
       "      <td>1924-07-10</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Mme Lowenfeld</td>\n",
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       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1 month</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Logbook</td>\n",
       "      <td>Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1924-06-10</td>\n",
       "      <td>1924-07-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22273</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1938-03-02</td>\n",
       "      <td>1938-04-02</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Ford Madox Ford</td>\n",
       "      <td>Ford, Ford Madox</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1 month</td>\n",
       "      <td>31.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lending Library Card</td>\n",
       "      <td>Sylvia Beach, Ford Madox Ford Lending Library ...</td>\n",
       "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
       "      <td>https://iiif.princeton.edu/loris/figgy_prod/b9...</td>\n",
       "      <td>1938-03-02</td>\n",
       "      <td>1938-04-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
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       "                                                     [key], {});\n",
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       "  "
      ],
      "text/plain": [
       "         event_type  start_date    end_date  \\\n",
       "30096  Subscription  1920-11-13  1920-12-13   \n",
       "3640     Supplement  1923-12-27  1924-03-27   \n",
       "4060        Renewal  1924-05-01  1924-06-01   \n",
       "31329       Renewal  1924-06-10  1924-07-10   \n",
       "22273  Subscription  1938-03-02  1938-04-02   \n",
       "\n",
       "                                             member_uris  \\\n",
       "30096  https://shakespeareandco.princeton.edu/members...   \n",
       "3640   https://shakespeareandco.princeton.edu/members...   \n",
       "4060   https://shakespeareandco.princeton.edu/members...   \n",
       "31329  https://shakespeareandco.princeton.edu/members...   \n",
       "22273  https://shakespeareandco.princeton.edu/members...   \n",
       "\n",
       "               member_names      member_sort_names  subscription_price_paid  \\\n",
       "30096  Mrs. Pierce Williams  Williams, Mrs. Pierce                     18.0   \n",
       "3640          Mme Lowenfeld         Lowenfeld, Mme                     20.0   \n",
       "4060      Colonel Thackeray     Thackeray, Colonel                     48.0   \n",
       "31329         Mme Lowenfeld         Lowenfeld, Mme                     48.0   \n",
       "22273       Ford Madox Ford       Ford, Ford Madox                     30.0   \n",
       "\n",
       "       subscription_deposit subscription_duration  subscription_duration_days  \\\n",
       "30096                   NaN               1 month                        30.0   \n",
       "3640                    NaN              3 months                        91.0   \n",
       "4060                    NaN               1 month                        31.0   \n",
       "31329                   NaN               1 month                        30.0   \n",
       "22273                  20.0               1 month                        31.0   \n",
       "\n",
       "       ...  item_volume item_authors item_year  item_notes  \\\n",
       "30096  ...          NaN          NaN       NaN         NaN   \n",
       "3640   ...          NaN          NaN       NaN         NaN   \n",
       "4060   ...          NaN          NaN       NaN         NaN   \n",
       "31329  ...          NaN          NaN       NaN         NaN   \n",
       "22273  ...          NaN          NaN       NaN         NaN   \n",
       "\n",
       "                source_type  \\\n",
       "30096               Logbook   \n",
       "3640                Logbook   \n",
       "4060                Logbook   \n",
       "31329               Logbook   \n",
       "22273  Lending Library Card   \n",
       "\n",
       "                                         source_citation  \\\n",
       "30096  Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...   \n",
       "3640   Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...   \n",
       "4060   Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...   \n",
       "31329  Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...   \n",
       "22273  Sylvia Beach, Ford Madox Ford Lending Library ...   \n",
       "\n",
       "                                         source_manifest  \\\n",
       "30096                                                NaN   \n",
       "3640                                                 NaN   \n",
       "4060                                                 NaN   \n",
       "31329                                                NaN   \n",
       "22273  https://figgy.princeton.edu/concern/scanned_re...   \n",
       "\n",
       "                                            source_image start_datetime  \\\n",
       "30096                                                NaN     1920-11-13   \n",
       "3640                                                 NaN     1923-12-27   \n",
       "4060                                                 NaN     1924-05-01   \n",
       "31329                                                NaN     1924-06-10   \n",
       "22273  https://iiif.princeton.edu/loris/figgy_prod/b9...     1938-03-02   \n",
       "\n",
       "      end_datetime  \n",
       "30096   1920-12-13  \n",
       "3640    1924-03-27  \n",
       "4060    1924-06-01  \n",
       "31329   1924-07-10  \n",
       "22273   1938-04-02  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subscription_events[subscription_events.subscription_volumes > 4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bzngB6VUGbNf"
   },
   "source": [
    "## report on number of events excluded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 256
    },
    "id": "Ymii5QsbGdH_",
    "outputId": "1ecbe0af-6f5b-42d3-9d79-752fed1f81bc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Excluded events, any type\n"
     ]
    },
    {
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       "          const element = document.querySelector('#df-ebfc895c-c47f-45c9-9724-036ea17c39a4');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "           label  count\n",
       "0       no_dates    383\n",
       "1       no_start     36\n",
       "2         no_end   2072\n",
       "3  partial_dates    190\n",
       "4  partial_start    108\n",
       "5    partial_end    136"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subset data for only those with complete start and end dates\n",
    "# - completely missing\n",
    "no_dates = events_df[events_df.start_date.isna() & events_df.end_date.isna()]\n",
    "no_start = events_df[events_df.start_date.isna() & events_df.end_date.notna()]\n",
    "no_end = events_df[events_df.start_date.notna() & events_df.end_date.isna()]\n",
    "\n",
    "# - partially missing\n",
    "partial_dates = events_df[(events_df.start_date.str.len() < 10) & (events_df.end_date.str.len() < 10)]\n",
    "partial_start = events_df[(events_df.start_date.str.len() < 10) & (events_df.end_date.str.len() >= 10)]\n",
    "partial_end = events_df[(events_df.start_date.str.len() >= 10) & (events_df.end_date.str.len() < 10)]\n",
    "\n",
    "excluded_events = pd.DataFrame([\n",
    "  ['no_dates', len(no_dates)],\n",
    "  ['no_start', len(no_start)],\n",
    "  ['no_end', len(no_end)],\n",
    "  ['partial_dates', len(partial_dates)],\n",
    "  ['partial_start', len(partial_start)],\n",
    "  ['partial_end', len(partial_end)]\n",
    "], columns=[\"label\", \"count\"])\n",
    "print('Excluded events, any type')\n",
    "excluded_events"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MsdZJZyWJhtD"
   },
   "source": [
    "### excluded borrow events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 256
    },
    "id": "wOEy0Ta9JEHC",
    "outputId": "521dccce-f339-4c63-e61c-12ba5ad0ab17"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Excluded borrows\n"
     ]
    },
    {
     "data": {
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       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>no_dates</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>no_start</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>no_end</td>\n",
       "      <td>929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>partial_dates</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>partial_start</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>partial_end</td>\n",
       "      <td>136</td>\n",
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       "  </tbody>\n",
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
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       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "           label  count\n",
       "0       no_dates      6\n",
       "1       no_start     36\n",
       "2         no_end    929\n",
       "3  partial_dates     55\n",
       "4  partial_start    108\n",
       "5    partial_end    136"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# report on excluded events for borrows\n",
    "excluded_borrows = pd.DataFrame([\n",
    "  ['no_dates', len(no_dates[no_dates.event_type == 'Borrow'])],\n",
    "  ['no_start', len(no_start[no_start.event_type == 'Borrow'])],\n",
    "  ['no_end', len(no_end[no_end.event_type == 'Borrow'])],\n",
    "  ['partial_dates', len(partial_dates[partial_dates.event_type == 'Borrow'])],\n",
    "  ['partial_start', len(partial_start[partial_start.event_type == 'Borrow'])],\n",
    "  ['partial_end', len(partial_end[partial_end.event_type == 'Borrow'])]\n",
    "], columns=[\"label\", \"count\"])\n",
    "print('Excluded borrows')\n",
    "excluded_borrows\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OSlWY7aKJleH"
   },
   "source": [
    "### excluded subscription events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 256
    },
    "id": "1yRp-4OFJo1N",
    "outputId": "dc779f27-e4a2-4374-8984-c360f6ffb49f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Excluded subscriptions\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>no_dates</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>no_start</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>no_end</td>\n",
       "      <td>635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>partial_dates</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
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       "\n",
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       "  </style>\n",
       "\n",
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       "\n",
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       "          const element = document.querySelector('#df-23d6292a-4f79-4ec9-a875-e7e2943950a6');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
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       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "          docLink.innerHTML = docLinkHtml;\n",
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       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "           label  count\n",
       "0       no_dates      1\n",
       "1       no_start      0\n",
       "2         no_end    635\n",
       "3  partial_dates    110\n",
       "4  partial_start      0\n",
       "5    partial_end      0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# report on excluded events for subscriptions \n",
    "excluded_subscriptions = pd.DataFrame([\n",
    "  ['no_dates', len(no_dates[no_dates.event_type.isin(['Subscription', 'Renewal', 'Supplement'])])],\n",
    "  ['no_start', len(no_start[no_start.event_type.isin(['Subscription', 'Renewal', 'Supplement'])])],\n",
    "  ['no_end', len(no_end[no_end.event_type.isin(['Subscription', 'Renewal', 'Supplement'])])],\n",
    "  ['partial_dates', len(partial_dates[partial_dates.event_type.isin(['Subscription', 'Renewal', 'Supplement'])])],\n",
    "  ['partial_start', len(partial_start[partial_start.event_type.isin(['Subscription', 'Renewal', 'Supplement'])])],\n",
    "  ['partial_end', len(partial_end[partial_end.event_type.isin(['Subscription', 'Renewal', 'Supplement'])])]\n",
    "], columns=[\"label\", \"count\"])\n",
    "print('Excluded subscriptions')\n",
    "excluded_subscriptions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sTECOFMAcPOe"
   },
   "source": [
    "# calculate borrowing capacity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ecl2BUjSeYNV"
   },
   "source": [
    "## generated filtered subscription set for members with cards\n",
    "\n",
    "Members with extant cards won't filter exactly as needed, since we know there are some members with partial borrowing history, but should a useful first look at borrowing capacity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 957
    },
    "id": "fpBg3AcFc602",
    "outputId": "e186552e-8514-4e83-8567-b833909af5c4"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_type</th>\n",
       "      <th>start_date</th>\n",
       "      <th>end_date</th>\n",
       "      <th>member_uris</th>\n",
       "      <th>member_names</th>\n",
       "      <th>member_sort_names</th>\n",
       "      <th>subscription_price_paid</th>\n",
       "      <th>subscription_deposit</th>\n",
       "      <th>subscription_duration</th>\n",
       "      <th>subscription_duration_days</th>\n",
       "      <th>...</th>\n",
       "      <th>membership_years</th>\n",
       "      <th>viaf_url</th>\n",
       "      <th>wikipedia_url</th>\n",
       "      <th>nationalities</th>\n",
       "      <th>addresses</th>\n",
       "      <th>postal_codes</th>\n",
       "      <th>arrondissements</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>notes</th>\n",
       "      <th>updated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1919-11-17</td>\n",
       "      <td>1919-12-17</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Claude Cahun / Mlle Lucie Schwob</td>\n",
       "      <td>Cahun, Claude</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1 month</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1919</td>\n",
       "      <td>http://viaf.org/viaf/19757660</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Claude_Cahun</td>\n",
       "      <td>France</td>\n",
       "      <td>Hôtel Saint Thomas D'Aquin, 3 rue du Pré aux C...</td>\n",
       "      <td>75007</td>\n",
       "      <td>7</td>\n",
       "      <td>48.85654, 2.33035</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-03T00:13:26+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1919-11-18</td>\n",
       "      <td>1919-12-18</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Henri Regnier</td>\n",
       "      <td>Regnier, Henri</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1 month</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1919</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9 rue de la Gare, Cachan</td>\n",
       "      <td>94230</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.79898, 2.32760</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31T05:00:10+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1919-11-18</td>\n",
       "      <td>1919-12-18</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Denise Ulmann</td>\n",
       "      <td>Ulmann, Denise</td>\n",
       "      <td>6.4</td>\n",
       "      <td>11.2</td>\n",
       "      <td>1 month</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1919</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>28 place Saint-Ferdinand, Paris</td>\n",
       "      <td>75017</td>\n",
       "      <td>17</td>\n",
       "      <td>48.87824, 2.28831</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31T01:29:35+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1919-11-19</td>\n",
       "      <td>1920-05-19</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Maurice Oerthel</td>\n",
       "      <td>Oerthel, Maurice</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6 months</td>\n",
       "      <td>182.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1920;1921;1919</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36 rue Saint-André des Arts, Paris</td>\n",
       "      <td>75006</td>\n",
       "      <td>6</td>\n",
       "      <td>48.85344, 2.34170</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-03T02:32:32+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Renewal</td>\n",
       "      <td>1920-05-19</td>\n",
       "      <td>1920-11-19</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Maurice Oerthel</td>\n",
       "      <td>Oerthel, Maurice</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6 months</td>\n",
       "      <td>184.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1920;1921;1919</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36 rue Saint-André des Arts, Paris</td>\n",
       "      <td>75006</td>\n",
       "      <td>6</td>\n",
       "      <td>48.85344, 2.34170</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-03T02:32:32+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9072</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1941-11-28</td>\n",
       "      <td>1941-12-28</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Jean Pierret</td>\n",
       "      <td>Pierret, Jean</td>\n",
       "      <td>20.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1 month</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147 rue d'Alésia, Paris</td>\n",
       "      <td>75014</td>\n",
       "      <td>14</td>\n",
       "      <td>48.82993, 2.32034</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-11-19T14:47:36+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9073</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1941-11-28</td>\n",
       "      <td>1941-12-28</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Robert Gillet</td>\n",
       "      <td>Gillet, Robert</td>\n",
       "      <td>20.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1 month</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1 rue Alphonse de Neuville, Paris</td>\n",
       "      <td>75017</td>\n",
       "      <td>17</td>\n",
       "      <td>48.88551, 2.30273</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-02T20:45:04+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9074</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1941-11-28</td>\n",
       "      <td>1941-12-28</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Bernard Lestocquoy</td>\n",
       "      <td>Lestocquoy, Bernard</td>\n",
       "      <td>25.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1 month</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80 rue Lauriston, Paris</td>\n",
       "      <td>75116</td>\n",
       "      <td>16</td>\n",
       "      <td>48.86765, 2.28811</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-11-19T14:47:34+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9075</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1941-11-28</td>\n",
       "      <td>1941-12-28</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Christianne Mingalon</td>\n",
       "      <td>Mingalon, Christianne</td>\n",
       "      <td>25.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1 month</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24 rue du Renard, Paris</td>\n",
       "      <td>75004</td>\n",
       "      <td>4</td>\n",
       "      <td>48.85995, 2.35299</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-05-05T19:08:48+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9078</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1941-12-05</td>\n",
       "      <td>1942-01-05</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Martine Lacorne</td>\n",
       "      <td>Lacorne, Martine</td>\n",
       "      <td>20.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1 month</td>\n",
       "      <td>31.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1941;1942</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>36 avenue de Joinville, Nogent-sur-Marne</td>\n",
       "      <td>94130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.83004, 2.46756</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-11-19T14:47:33+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1920 rows × 51 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-04b2f2bb-1277-451b-bb21-6856c66648f1')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
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       "      \n",
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       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
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       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-04b2f2bb-1277-451b-bb21-6856c66648f1');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
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       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
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      ],
      "text/plain": [
       "        event_type  start_date    end_date  \\\n",
       "0     Subscription  1919-11-17  1919-12-17   \n",
       "19    Subscription  1919-11-18  1919-12-18   \n",
       "20    Subscription  1919-11-18  1919-12-18   \n",
       "22    Subscription  1919-11-19  1920-05-19   \n",
       "23         Renewal  1920-05-19  1920-11-19   \n",
       "...            ...         ...         ...   \n",
       "9072  Subscription  1941-11-28  1941-12-28   \n",
       "9073  Subscription  1941-11-28  1941-12-28   \n",
       "9074  Subscription  1941-11-28  1941-12-28   \n",
       "9075  Subscription  1941-11-28  1941-12-28   \n",
       "9078  Subscription  1941-12-05  1942-01-05   \n",
       "\n",
       "                                            member_uris  \\\n",
       "0     https://shakespeareandco.princeton.edu/members...   \n",
       "19    https://shakespeareandco.princeton.edu/members...   \n",
       "20    https://shakespeareandco.princeton.edu/members...   \n",
       "22    https://shakespeareandco.princeton.edu/members...   \n",
       "23    https://shakespeareandco.princeton.edu/members...   \n",
       "...                                                 ...   \n",
       "9072  https://shakespeareandco.princeton.edu/members...   \n",
       "9073  https://shakespeareandco.princeton.edu/members...   \n",
       "9074  https://shakespeareandco.princeton.edu/members...   \n",
       "9075  https://shakespeareandco.princeton.edu/members...   \n",
       "9078  https://shakespeareandco.princeton.edu/members...   \n",
       "\n",
       "                          member_names      member_sort_names  \\\n",
       "0     Claude Cahun / Mlle Lucie Schwob          Cahun, Claude   \n",
       "19                       Henri Regnier         Regnier, Henri   \n",
       "20                       Denise Ulmann         Ulmann, Denise   \n",
       "22                     Maurice Oerthel       Oerthel, Maurice   \n",
       "23                     Maurice Oerthel       Oerthel, Maurice   \n",
       "...                                ...                    ...   \n",
       "9072                      Jean Pierret          Pierret, Jean   \n",
       "9073                     Robert Gillet         Gillet, Robert   \n",
       "9074                Bernard Lestocquoy    Lestocquoy, Bernard   \n",
       "9075              Christianne Mingalon  Mingalon, Christianne   \n",
       "9078                   Martine Lacorne       Lacorne, Martine   \n",
       "\n",
       "      subscription_price_paid  subscription_deposit subscription_duration  \\\n",
       "0                         4.0                   NaN               1 month   \n",
       "19                        4.0                   5.6               1 month   \n",
       "20                        6.4                  11.2               1 month   \n",
       "22                       16.0                   NaN              6 months   \n",
       "23                       16.0                   NaN              6 months   \n",
       "...                       ...                   ...                   ...   \n",
       "9072                     20.0                  50.0               1 month   \n",
       "9073                     20.0                  50.0               1 month   \n",
       "9074                     25.0                  50.0               1 month   \n",
       "9075                     25.0                  50.0               1 month   \n",
       "9078                     20.0                  50.0               1 month   \n",
       "\n",
       "      subscription_duration_days  ...  membership_years  \\\n",
       "0                           30.0  ...              1919   \n",
       "19                          30.0  ...              1919   \n",
       "20                          30.0  ...              1919   \n",
       "22                         182.0  ...    1920;1921;1919   \n",
       "23                         184.0  ...    1920;1921;1919   \n",
       "...                          ...  ...               ...   \n",
       "9072                        30.0  ...              1941   \n",
       "9073                        30.0  ...              1941   \n",
       "9074                        30.0  ...              1941   \n",
       "9075                        30.0  ...              1941   \n",
       "9078                        31.0  ...         1941;1942   \n",
       "\n",
       "                           viaf_url  \\\n",
       "0     http://viaf.org/viaf/19757660   \n",
       "19                              NaN   \n",
       "20                              NaN   \n",
       "22                              NaN   \n",
       "23                              NaN   \n",
       "...                             ...   \n",
       "9072                            NaN   \n",
       "9073                            NaN   \n",
       "9074                            NaN   \n",
       "9075                            NaN   \n",
       "9078                            NaN   \n",
       "\n",
       "                                  wikipedia_url  nationalities  \\\n",
       "0     http://en.wikipedia.org/wiki/Claude_Cahun         France   \n",
       "19                                          NaN            NaN   \n",
       "20                                          NaN         France   \n",
       "22                                          NaN            NaN   \n",
       "23                                          NaN            NaN   \n",
       "...                                         ...            ...   \n",
       "9072                                        NaN            NaN   \n",
       "9073                                        NaN            NaN   \n",
       "9074                                        NaN            NaN   \n",
       "9075                                        NaN            NaN   \n",
       "9078                                        NaN         France   \n",
       "\n",
       "                                              addresses  postal_codes  \\\n",
       "0     Hôtel Saint Thomas D'Aquin, 3 rue du Pré aux C...         75007   \n",
       "19                             9 rue de la Gare, Cachan         94230   \n",
       "20                      28 place Saint-Ferdinand, Paris         75017   \n",
       "22                   36 rue Saint-André des Arts, Paris         75006   \n",
       "23                   36 rue Saint-André des Arts, Paris         75006   \n",
       "...                                                 ...           ...   \n",
       "9072                            147 rue d'Alésia, Paris         75014   \n",
       "9073                  1 rue Alphonse de Neuville, Paris         75017   \n",
       "9074                            80 rue Lauriston, Paris         75116   \n",
       "9075                            24 rue du Renard, Paris         75004   \n",
       "9078           36 avenue de Joinville, Nogent-sur-Marne         94130   \n",
       "\n",
       "      arrondissements        coordinates notes                    updated  \n",
       "0                   7  48.85654, 2.33035   NaN  2020-01-03T00:13:26+00:00  \n",
       "19                NaN  48.79898, 2.32760   NaN  2019-12-31T05:00:10+00:00  \n",
       "20                 17  48.87824, 2.28831   NaN  2019-12-31T01:29:35+00:00  \n",
       "22                  6  48.85344, 2.34170   NaN  2020-01-03T02:32:32+00:00  \n",
       "23                  6  48.85344, 2.34170   NaN  2020-01-03T02:32:32+00:00  \n",
       "...               ...                ...   ...                        ...  \n",
       "9072               14  48.82993, 2.32034   NaN  2019-11-19T14:47:36+00:00  \n",
       "9073               17  48.88551, 2.30273   NaN  2020-01-02T20:45:04+00:00  \n",
       "9074               16  48.86765, 2.28811   NaN  2019-11-19T14:47:34+00:00  \n",
       "9075                4  48.85995, 2.35299   NaN  2021-05-05T19:08:48+00:00  \n",
       "9078              NaN  48.83004, 2.46756   NaN  2019-11-19T14:47:33+00:00  \n",
       "\n",
       "[1920 rows x 51 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_subs = subscription_events.copy()\n",
    "member_subs[['first_member_uri','second_member_uri']] = member_subs.member_uris.str.split(';', expand=True)\n",
    "# handle multiple member uris (joint accounts) in subscriptions by merging on the first one (has card should be same)\n",
    "member_subs = pd.merge(left=member_subs, right=members_df, left_on=\"first_member_uri\", right_on=\"uri\")\n",
    "member_subs = member_subs[member_subs.has_card]\n",
    "member_subs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ggKvnNyjcCl8"
   },
   "source": [
    "## calculate total books out and subscription volumes per day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "id": "bFra7PjN-L5a",
    "outputId": "7a5328e2-d869-47bb-c30d-699b9c72a4b1"
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   "outputs": [
    {
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      "text/plain": [
       "           date  books_out  subscription_volumes  card_subscription_volumes\n",
       "0    1919-11-17          0                   3.0                        1.0\n",
       "1    1919-11-18          3                   9.0                        4.0\n",
       "2    1919-11-19          5                  10.0                        5.0\n",
       "3    1919-11-20          5                  11.0                        5.0\n",
       "4    1919-11-21          5                  13.0                        5.0\n",
       "...         ...        ...                   ...                        ...\n",
       "8321 1942-08-29         17                   1.0                        1.0\n",
       "8322 1942-08-30         17                   1.0                        1.0\n",
       "8323 1942-08-31         17                   1.0                        1.0\n",
       "8324 1942-09-01         17                   1.0                        1.0\n",
       "8325 1942-09-02         17                   1.0                        1.0\n",
       "\n",
       "[8326 rows x 4 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import date, timedelta\n",
    "# arbritrary end date while testing\n",
    "#end_day = date(1925, 6, 1)\n",
    "# go through end of last subscription end date\n",
    "#end_day = subs_end_date\n",
    "one_day = timedelta(days=1)\n",
    "# start with the earliest start date\n",
    "day = date_events.start_datetime.iloc[0]\n",
    "\n",
    "dates = []\n",
    "books_out = []\n",
    "subscription_vols = []\n",
    "card_subscription_vols = []\n",
    "\n",
    "while day < subs_end_date:\n",
    "    dates.append(day)\n",
    "    # get total number of borrowing events spanning this date; include start date, exclude end (to avoid double counting)\n",
    "    books_out.append(len(borrow_events[(borrow_events.start_datetime <= day) & (borrow_events.end_datetime > day)]))\n",
    "    # total number of volumes from active subscriptions for this date\n",
    "    subscription_vols.append(subscription_events[(subscription_events.start_datetime <= day) & (day < subscription_events.end_datetime)].subscription_volumes.sum())\n",
    "    # total number of volumes from subscriptions for members with extant cards\n",
    "    card_subscription_vols.append(member_subs[(member_subs.start_datetime <= day) & (day < member_subs.end_datetime)].subscription_volumes.sum())\n",
    "\n",
    "    day += one_day\n",
    "\n",
    "borrowing_df  = pd.DataFrame()\n",
    "borrowing_df['date'] = dates\n",
    "borrowing_df['books_out'] = books_out\n",
    "borrowing_df['subscription_volumes'] = subscription_vols\n",
    "borrowing_df['card_subscription_volumes'] = card_subscription_vols\n",
    "borrowing_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5vjiPm22cN6p"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DfHi6wmgewgL"
   },
   "source": [
    "### chart: daily books out vs total subscription volumes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "id": "WUzdLGLR-xHa",
    "outputId": "704ebea4-b0a1-43c5-cd9c-873f3826ccbd"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# get current axis so we can plot two series on the same axis\n",
    "ax = plt.gca()\n",
    "\n",
    "borrowing_df.plot(kind='line',x='date',y='books_out',ax=ax, figsize=(25,7), title='books out vs. total subscription volumes')\n",
    "borrowing_df.plot(kind='line',x='date',y='subscription_volumes', color='green', ax=ax)\n",
    "\n",
    "plt.savefig('sco_borrowing_capacity.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 339
    },
    "id": "69APgwLDbikD",
    "outputId": "6464c401-0a61-401a-8639-ce4d057b37eb"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-ee3f9b595999440eb0543d1817c3e1b5\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-ee3f9b595999440eb0543d1817c3e1b5\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-ee3f9b595999440eb0543d1817c3e1b5\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"mark\": {\"type\": \"line\", \"stroke\": \"#5276A7\", \"strokeWidth\": 1}, \"encoding\": {\"x\": {\"axis\": {\"title\": \"date\"}, \"field\": \"date\", \"type\": \"temporal\"}, \"y\": {\"axis\": {\"title\": \"books out\", \"titleColor\": \"#5276A7\"}, \"field\": \"books_out\", \"type\": \"quantitative\"}}, \"height\": 275, \"width\": 1200}, {\"mark\": {\"type\": \"line\", \"stroke\": \"green\", \"strokeWidth\": 1}, \"encoding\": {\"x\": {\"axis\": {\"title\": \"date\"}, \"field\": \"date\", \"type\": \"temporal\"}, \"y\": {\"axis\": {\"title\": \"subscription volumes\", \"titleColor\": \"green\"}, \"field\": \"subscription_volumes\", \"type\": \"quantitative\"}}, \"height\": 275, \"width\": 1200}], \"data\": {\"name\": \"data-c5a7cadb47d0fbf240baa72012612dfd\"}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": 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\"1942-07-22T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-23T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-24T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-25T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-26T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-27T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-28T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-29T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-30T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-31T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-01T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-02T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-03T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-04T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-05T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-06T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-07T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-08T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-09T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-10T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-11T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-12T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-13T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-14T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-15T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-16T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-17T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-18T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-19T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-20T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-21T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-22T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-23T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-24T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-25T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-26T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-27T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-28T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-29T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-30T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-31T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-09-01T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-09-02T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# same plot but with altair\n",
    "\n",
    "import altair as alt\n",
    "\n",
    "# allow large datasets\n",
    "alt.data_transformers.enable('default', max_rows=None)\n",
    "\n",
    "chart_height = 275\n",
    "\n",
    "base = alt.Chart(borrowing_df).encode(\n",
    "    alt.X('date:T', axis=alt.Axis(title='date'))\n",
    ").properties(\n",
    "    width=1200,\n",
    "    height=chart_height\n",
    ")\n",
    "\n",
    "line_books_out = base.mark_line(stroke='#5276A7', strokeWidth=1).encode(\n",
    "    alt.Y('books_out',\n",
    "          axis=alt.Axis(title='books out', titleColor='#5276A7'))\n",
    ")\n",
    "\n",
    "line_subs_vols = base.mark_line(stroke='green', strokeWidth=1).encode(\n",
    "    alt.Y('subscription_volumes',\n",
    "          axis=alt.Axis(title='subscription volumes', titleColor='green'))\n",
    ")\n",
    "\n",
    "line_books_out + line_subs_vols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
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     "height": 424
    },
    "id": "KnnO3RAv0hha",
    "outputId": "9947acae-454b-49ec-a768-5488d595957a"
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   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>date</th>\n",
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       "      <th>card_subscription_volumes</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1919-11-17</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1919-11-18</td>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1919-11-19</td>\n",
       "      <td>5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>1919-11-20</td>\n",
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       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1919-11-21</td>\n",
       "      <td>5</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
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       "      <th>8321</th>\n",
       "      <td>1942-08-29</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8322</th>\n",
       "      <td>1942-08-30</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8323</th>\n",
       "      <td>1942-08-31</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8324</th>\n",
       "      <td>1942-09-01</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>8325</th>\n",
       "      <td>1942-09-02</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8326 rows × 4 columns</p>\n",
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       "                                                     [key], {});\n",
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      ],
      "text/plain": [
       "           date  books_out  subscription_volumes  card_subscription_volumes\n",
       "0    1919-11-17          0                   3.0                        1.0\n",
       "1    1919-11-18          3                   9.0                        4.0\n",
       "2    1919-11-19          5                  10.0                        5.0\n",
       "3    1919-11-20          5                  11.0                        5.0\n",
       "4    1919-11-21          5                  13.0                        5.0\n",
       "...         ...        ...                   ...                        ...\n",
       "8321 1942-08-29         17                   1.0                        1.0\n",
       "8322 1942-08-30         17                   1.0                        1.0\n",
       "8323 1942-08-31         17                   1.0                        1.0\n",
       "8324 1942-09-01         17                   1.0                        1.0\n",
       "8325 1942-09-02         17                   1.0                        1.0\n",
       "\n",
       "[8326 rows x 4 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "borrowing_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
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     "height": 424
    },
    "id": "Q9OGyVD60scB",
    "outputId": "2e9b016f-64ec-407d-9438-2c283d7ad2ee"
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       "      <td>1919-11-21</td>\n",
       "      <td>5.0</td>\n",
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       "      <th>8322</th>\n",
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       "      <th>8323</th>\n",
       "      <td>1942-08-31</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Subscription volumes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8324</th>\n",
       "      <td>1942-09-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Subscription volumes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8325</th>\n",
       "      <td>1942-09-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Subscription volumes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>16652 rows × 3 columns</p>\n",
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      "text/plain": [
       "           date  volumes              activity\n",
       "0    1919-11-17      0.0             Books out\n",
       "1    1919-11-18      3.0             Books out\n",
       "2    1919-11-19      5.0             Books out\n",
       "3    1919-11-20      5.0             Books out\n",
       "4    1919-11-21      5.0             Books out\n",
       "...         ...      ...                   ...\n",
       "8321 1942-08-29      1.0  Subscription volumes\n",
       "8322 1942-08-30      1.0  Subscription volumes\n",
       "8323 1942-08-31      1.0  Subscription volumes\n",
       "8324 1942-09-01      1.0  Subscription volumes\n",
       "8325 1942-09-02      1.0  Subscription volumes\n",
       "\n",
       "[16652 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# flatten for altair to get a better legend\n",
    "books_out = borrowing_df.rename(columns={\"books_out\": \"volumes\"})[[\"date\", \"volumes\"]]\n",
    "books_out[\"activity\"] = \"Books out\"\n",
    "\n",
    "subs_vols = borrowing_df.rename(columns={\"subscription_volumes\": \"volumes\"})[[\"date\", \"volumes\"]]\n",
    "subs_vols[\"activity\"] = \"Subscription volumes\"\n",
    "\n",
    "alt_borrowing = pd.concat([books_out, subs_vols])\n",
    "alt_borrowing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure - Borrowing capacity analysis; subscription volume limit utilization for members with cards."
   ]
  },
  {
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   "execution_count": 48,
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volumes\"}, {\"date\": \"1942-07-14T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-07-15T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-07-16T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-07-17T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-07-18T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-07-19T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-07-20T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-07-21T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-07-22T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-07-23T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-07-24T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-07-25T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-07-26T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-07-27T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-07-28T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-07-29T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-07-30T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-07-31T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-01T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-02T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-03T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-04T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-05T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-06T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-07T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-08T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-09T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-10T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-11T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-12T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-13T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-14T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-15T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-16T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-17T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-18T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-19T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-20T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-21T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-22T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-23T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-24T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-25T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-26T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-27T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-28T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-29T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-30T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-08-31T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-09-01T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}, {\"date\": \"1942-09-02T00:00:00\", \"volumes\": 1.0, \"activity\": \"Subscription volumes\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "chart = alt.Chart(alt_borrowing).encode(\n",
    "    alt.X('date:T', axis=alt.Axis(title='Duration of the Lending Library')),\n",
    ").properties(\n",
    "    width=1200,\n",
    "    height=chart_height\n",
    ")\n",
    "\n",
    "books_out_vs_subs = chart.mark_line(strokeWidth=1).encode(\n",
    "    alt.Y(\"volumes\", axis=alt.Axis(title='Number of volumes')),\n",
    "    color=alt.Color(\"activity\", title=\"\", scale=alt.Scale(range=[\"#5276A7\", \"green\"]))\n",
    ")\n",
    "books_out_vs_subs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "0aVwByP65OWl"
   },
   "outputs": [],
   "source": [
    "# figure 7\n",
    "books_out_vs_subs.save(\"fig7-booksout_subs_volumes.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pTspZp_tfMMD"
   },
   "source": [
    "### chart: daily books out vs subscription volumes for members with extant card"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "id": "M3vVQoEZbTOx",
    "outputId": "151489ca-4a41-4a35-fcf1-50f98b322993"
   },
   "outputs": [
    {
     "data": {
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d6xo/YN3aHNdv//mBlm9oTH7WWtnc/vxqIpHK9Wx2qW00vOXzVm3WA9MXeWMLrb/38O102G5Dkvcl4ZwWrN4iyftCIJ+wOVxxnNo2JF9tJwhMJKvN62paq86LbdERyNVnG4VLW7mf5j5R2QwAAAAAAICy8no2t76PmHVoCHXMDS/quqffl5S9ujVTwNo7JZTesNULm194f4V+88/39Yfn5yQ/a+3Z3H4/NVHLWD1dqqByOxoJKpu95V+66V/J6uNwVXdL3KkmEglVNksX3T9Tkhe659PuY+2W/CYRzKZ1gkCnxuZ4m9YmZx7wcUnS9n3r0m6br2Y/4K9UVXl3kvwyJc0DntqWRiJsBgAAAAAAQJklnJJ9kiUvqKpU7jdz0Tp9tL6xzbLmUGVrtrA5U8CaukWwv2D9lZtaeyFny9Yq2bM5lmGCwPV+MC55PZl/+ve39eHKTXpv+UbVRi0ZiodDweZYIhleZzN/1ZbSBx5UNvttNOp7tAaWx+09TFLpvZaDymbC5tIlv0xJ89nA3j3aLevOl5ywGQAAAAAAoAISzrWp9I0kA8byJ1FfuulfOvTa5zN+nql9R8TyCyN71kbU1NJ2wrl023V0z+ZEso1G257NPUKtDSb/e4Fue2mejvz1C4onnNZuaU6eg3NOn/pYX0nSXsO3yysUn71kXcnjDl+mcM9mqfWLgZYSq8FjKV8OoHjBj2wkTZKaPmzuvtecsBkAAAAAAKACnGvbwzh4XansrymWOZzMVCXbMKh35srm0DZ96mrUGPN6FQdtJOIJJ+ecXp23JtliI2PP5kpNEJhso+Edd0NjTFJrpbMkLVrbdlLGw3bbPnk9YnGnof16qlePqHYe0ievUHzeqs2SpPeuPqbocYevbWobjeCLgXiJfa5bErTRKJcgPE73fPeoiejTHx/QZlklvlDaVhA2AwAAAAAAVECiXc9m7/9KBlGL1qRv8ZBpsjmv6jh3Be129bXJyuYfPfKmJC80n71kvU65ZZpumTpXUvrK5kikcoFnEGIHlczXPvWepLaTtjWnhPA9opFkheodL8/XC++v1JbmuGoilpxwMJu3lm6Q1HYyv2K1ttFoHW8QnJca0AeVzannj8IFdyJTZ5PUsLk7z81I2AwAAAAAAFABTm0riiORylY2S9LCDGFzXU3msDmWoYI2nKv17VmrJr+yORAxaXNT22XpVHJixERKZXOgJUvaV1sTSd6XNxavTy6P+O0+cn0Z0CMa0ahh/YodsqTWa7tyU5NiCZdS2ezdq3BA3xJPaOm6rcnxbW6K5TxGcF+XrtuaY03kkuvxvfTo3fSJIb2T72mjAQAAAAAAgLJKrWxONylduW1sbJ0YL1yFObhvXdr1a6KZ+ykH2/esjaiuJtKuTUfErF0wm67w01sv99iL0TpBYNuIa0tz5hC8R9TSthVJtq/I8W1A3Lm0fXoLERz+v25+WZLS9myOhSrO//uP03TgNc/pJ397Szc+N0ejrnxKazc3Zz1G0EZjY1NMf5u1tKTxwm+jkaG0uTYa0XF77ZB8T9gMAAAAAACAsnHOeT2bw5XNyUnpKnfccCDcp0dN8vWuQ/umXT+aRz/liJnqaqPJfR+1x1BJUv9etUrdMl0YZ1a58C3Y74BetZKkY0fvkG11SV5bieBLgPBwg5A3VyuNRMJl7IGdr9TN63uk6dmccMnge9Yib1LCe/69UJNfXShJ2pSjujkWqu5+/t0VJY23uwseiWx3/cIjd9Hlx+7eZv3uiLAZAAAAAACgzD5YsUmS9B8/JJRaezaXO3hNhMLiIJxctGaLNvph5L4jB2bctiaSpbLZj9aiZl5lc0tcJ9z0Lz399nL/WO3PpSMrm0dPeko/fuwtb4wR04iB9aqLRnT90+9l3a42Gkm2NFkcmjwwGgp5d/7hE/rVk++m3T6WcMlAuFipE831rGlf2Xz9M+9rlx89oeffaxsUL1vfKKm1LUsmLaH2KC1MEliSXD2bJa9P+JgR/SV170kZCZsBAAAAAADK7IX3VkqSpr6/MrksCBjLHTaHK3GDPr3zV29OLrvlqHGDbwAAIABJREFUjE+32+af3ztUj3zrQK9nc6YJAv1gLRLxwubmWCJZYSu1Vm+32STdBIEVqmze0Nha2RuJmExeb+jfPTcn63bHjxmWti9zTWhivnjC6Q9TPky7fTzhcga9uaRep/5+ZbbktTaRvCA84aQX31+Vdh+JHIFm+L7GuvOMdWXQWtmc/b639mXvvmFzTe5VAAAAAAAAUIh0oWuwrNwx1KLQpIAtfsAYnvRvQJr+wp/cvo8kL2BtiSfUcNnjkqS5P/9CMjALTuHjg3qpriaquas2t9lH3Ln82mio/BMEpobFNRFTxHJf2/MO/YR69ahpU/UbCCqKm2PZg9mEc4qW2EYjVfgeBRMEBlZtampz7ECm9ierNzXp01f/s82yUtt+dHcu2bM5+3od0Sqnq6OyGQAAAAAAoAMkg6gyF5ne99qi5OsgZN7aknmCvLBoxNTY0jqgjaFq4X71XrXtNSeN1nF7t++FHE+4vEJks/KHb6lhccRMZqaEkw7bbUjG7Wr9IDeWJWzemmVyQck772iJlc2pBvYKh81t970hNOlj+DrGM1SkT3mvtZp+cB9vYsj9PpG5lQpyy6dns1S5VjnbEsJmAAAAAACADlCpIKol1CLhyr++pZZ4ImdgGpi1aJ1mL1mffL9mS7Mk6Sd/e0vPvL1c+39ioPYY1k+H7do+wE0416aUOFPVZ8QPgcspNUyPRswPtZ0G9u6h4QPq024XtKhI1zokCJC35Lh2CZe7X3IuqRXgA0JhczTa9rPNGSYCzNQZo2/PcCMD78KnC9eRv2TYnGdlMz2bAQAAAAAAUDbp2hZUqp/riAG92rxfsnZrMoD++Yl7Zd023PdYkjb57//0r/mSpIF+ewfzJwkMS61szpTDRSLt216UqiUlaY2a103XOa/quUc0ogsO37l1DCYdvMtgHeqH5unCwKCieHNz+nA3EE84RUssbA636vjGZ0aqX31rQJxa2bypqTX87lnbeg8y9druU9e6r1WbmrOui/y0NozJfuN7+D8jzd24RzZhMwAAAAAAQJml79kchM3F79c5p1te+FArN7b28U3d3VtLNySXHf6pzC0l0pn6wco278MVt5/bY2ibz+KJtm0d0vVrloLK5nL3bE45RsT+n73zDpObOrv4kaZtX6+97t3GvVfAYDAYAsR0CDVAgIRAEko+ApgaU0MIoYUeTDMtBBzA2NjYNGPccLfXbd3Ler29T5X0/aG5miuNNH13Z9fv73n8WKNydUcjzWqOzj0vREGAAgX+gAy7TcDvTw2JzRP7dcScG4/HuD4FAMJjOFg/AeDl70KFAfm8ZEYqCgTWBB3k15/UDw+cO1x37IwRHczZfMHYHrrIEyv3rFmWs9n7JWKHnW/RPvZMhw1AdHd8e4bEZoIgCIIgCIIgCIIgiBTDHMH3njNUm8d0qmRcvtuO1ONvX23HbR+u1+YZs3uP1Lq5jNnI6tjlE3vrXv9j0Q7d69wMhzbNMpwZshKbs1lohhgNs2MoCIAsq65nh03UucuN6584oFPY9ixiY8m2o9q8hz7fErZeKgoEVgfFZj6rWeuHoUAgE5u75Lp0863FZv35IApAUxS3NhGZUIxG5M89O+gqb7KIPjkWILGZIAiCIAiCIAiCIAgixTBRavqwkBtYKxCYRLtM3K1xh4rGGZ2sAVnRhv1H00TvOHNQ+D649pxcdAZzbTIkWdG9F6t9CUh9dMh/1x4KmycKAo7UuvHDznI4bKJOEDbqsvlZDhixieEymZmgm4oCgVWN6ufXITtcbDY23RAULpkrO1rf7v5ks25eQZYT1U3+sHWJ2NGupyjrZTmDzuYYC3S2R0hsJgiCIAiCIAiCIAiCSDHMbczn76aiQCBz3/KCsGSISJBkhXM2R8YoIAOAxPXPFUFslhVF5xi2clEnqcuGUdHgDXNgMzYeqkVAVjObeUH4qsl9orZr5lbuV5gdNk9Wko/ROH9MDwDAqYPCY06M7llvMN+5T0d9NrdZXMamQzW66I/LJvZCfqYDtW4Sm5Mh1gKBLruoOsm9JDYTBEEQBEEQBEEQBEEQKSIQFIBtOrE5+cxmJojuOFqvFZljouPuJ34JAFi1tyrkOI4ijmVwAvLY3h0wdVChzjHrtHFis1MvNh+oatJnJ1vsK9WZzY0WEQV8bIbDLsDBVfG7ZEKvqO2auZV75GfqXu8qa0Bloy+siF+8nDiwE/Y9OQN9OmVFXG9Y9zxtOj/Tofs8ZFnBlsO18HAuWmM288xzhsHlsMHrP3YL1qUCrTxglI9dEARkOe2U2UwQBEEQBEEQBEEQBEGkDibY2jnBkwlVcooCjP/88QZtXzZR0MTSpTvL8d81B9V9RlGbmXP5ysm94bAJCEj6HGaHSf8ZR+s8iEFrVsXmFGqdbouIAj4Fwy6KUfN1ec4f00P3XhnGqIoznvkBiqIXtpsTbyD0XkVRwJWTQxnbR+s9OPdfyzDz003aPGN/bYIAl13UtUPED3PwR7ueADVKw+2nzGaCIAiCIAiCIAiCIAjCAo9fiiuKgLmNebcsEz9ZcbhE8Ekh1fabYCG7gEmG8N6KxuA+I7cnCAK2PXI2HrtwFERBgKQoemezPeRmPiUY+ZDrsuPq4/tAgN6xbJnZLKQ2s9ltcI1+cvOJ6n44ITDeTOWnLh2tc25fc0JfANb9TjazOVamDe4S2qcg4KHzRuDN30wEAJTWqnEZ6w/WaOvUefTnqCiqudu7yxpQ76EojUSJdaQAoIrNjRSjkRiCIPxZEIQiQRC2CILwoSAIGYIg9BcEYZUgCLsEQfiPIAjhSecEQRAEQRAEQRAEQRBtiAtf+gljHv465vU1ZzNnt2XuyPNf/Cnhfjz25TZt2hOMRpBkOSzWgRUqi8WBm+m0ac5oWVZ0MR98gcBu+RkAgDOHd0V+pgMBWdbFaFhnNgtIZX1Ao7O5dzDLmD8EfE8Kc1xR23TYRGQ77drrgmDhvtYSm7vmueCyi+hfGIrZEEV1v51z1M/h7wu3q33hPuPfz1mra0cUBKzeW4WSWg9GzYr9/CX0xJqBDoBiNBLdUBCEngBuAzBRUZSRAGwArgDwdwDPKopyHIBqADemoqMEQRAEQRAEQRAEQRCtxfbS+rjWN3M2T+7fMel+rNhTabovo/jpCjqS45FEbaKAJp+ki/ngmy3MceHLW0/GIxeOhMtug1/Su6BbytnM5xLfPn0QuuZlhHWATa6493R8c+eppu0svGOqNm0TBWS7QmIzixeRLOI/mjtG47M/noRFd5yCyyaFYjOYqGz8rCMJ3y3lwG7/BGM0Yvjcs5w2NPmO3RgNe/RVom6fKQiCH0AWgCMATgdwVXD5OwBmAXglyf0QBEEQBEEQBEEQBEG0GaRgSDHvOO6cG91hm9i+wsVmFpkQjya6Zl813H4J4x5drM0zxkuP7JkPAHA5VDGWL04XKbM5hcZm+AMhBXhglxxtevuROm2aif3dDQX+eHp20C/LdoViNJjYbO1sjqPDCWDWb/YZxyM2t1S2dHsnLmezy466OCJ32hsJXxqKohwG8DSAA1BF5loAawHUKIrC5PtDAHqabS8Iwk2CIKwRBGFNeXl5ot0gCIIgCIIgCIIgCIJIO8yczVlOOwqyHOial1rROSArurgOAGjwqtJMLAXNGGaF96zEVibG8ttYuT5T72wOic38Hr2cCG0Uks3gI0IA9fNhuByq8GwsuMewtYKImxN0XhvFZT5r2gg5m1MDOwti+dizHMe2szmZGI0CABcA6A+gB4BsAGfHur2iKK8rijJRUZSJnTt3TrQbBEEQBEEQBEEQBEEQLcbhGndM60kSy2zWq1NTBhYiN8OR0j5JkhK2Hy1qIkmtUbEQiZkIu2DzEW2e1a5+LK7A+gM1Fkvjhy+SaOXcHd0rP2o7zqA9meVbZ3GircvGYjTM37/YCiKuPdgn42ed47IOLjB28965mwAAd368ETNe+NHy8yX0sMMUi1M8y2WjzOYEOQPAXkVRyhVF8QOYC+AkAB0EQWBneS8Ah5PsI0EQBEEQBEEQBEEQRFrw7bajMa1n5mwGAKTQ5TtjVHdtX1YO1mQNuBZaKw5Vq6L7z/uquZ0lt69Y4TObrTTfGaN7RG1HEAQ8cdEozLlxMgC1SCDDaRcjOrJb0tm86I5T8No1E7TXxl3z/RYE/TlndJt/uPogAODTdYdQVFKnO5aENew8iK1AIInNiXIAwAmCIGQJ6pk7HcBWAN8BuDS4znUAPk+uiwRBEARBEARBEARBEOmBTYxNSmE5ykaxTxQEpMpMurCoFBe+9BM2HqqB3WYhNie5j0g5zKneV6zoYjQsRN9Ibl+eq47vgwl9wws3OmwibIKgOZs3H6rFH95fqy1vSWfzkG65OGtEN+11pPMn02HD9VP6RWzvytdXatOpjDdpz2iHKYaPPdtpR1WjD2c9uxTuY1B0TiazeRWATwCsA7A52NbrAO4B8H+CIOwC0AnA7BT0kyAIgiAIgiAIgiAIotUpyIotAsPKbSwK1tEUsTCgMBsA0LdTFiRZwYaDNdhV1hDB2Zy4KNo1z4WLxpuW4jJ1TPOZxzwje+YBSO598/h1MRopaTIMh02AKAias/u2j9ZjweZSbbkxyqIliXQUA5ICu03EvD+djJnnDAUAPHf5WG15XoYdK/ZUaq9JbI4NBczZHP1zZ0U0dxytx4/Fx16duqRqZyqK8ldFUYYqijJSUZRrFEXxKoqyR1GUyYqiHKcoyq8URfGmqrMEQRAEQRAEQRAEQRCtiScQm1NRkmVTQZIXMBPBJgqYMao7yur0couV+JmMJPr8FePgspsXnzPbXUG203TdM4eprtxk3jePLxA9szlZHHYRohgSYx0G53hjKzpWIwnEflmGwyZgVK983HzqQADA2N4dtOVDuuXq1i+v9+LeuZtwpDa2LPJjFhaBHsPpdsqgY7s2XVJiM0EQBEEQBEEQBEEQxLGE1y9HXwmqGJnhCBdqI+UAx0JTsF23Xy92WsV7JCPGdrQQjwHgumBUQyb3HnNc5sI0ixS2KrYXL3zOcHNFJztEEQ6bqAnbfC4yAGw8mLqCh/HSp2OW7jVzekuyAkUB7IZzwWkPveaFegD4bH0JPlx9EB+uOtBMvW0fxJGigdyMkMP/WPSNk9hMEARBEARBEARBEAQRAZkTSf0xCqbVjT5TsTaZzGZFUXC4xo1MZ7icY+lsjkOMNbYRSWzulpcBQB8rYhQ5GSzfOFWRDfoYjdSqzb0KMgGoTmaX3QafZC4217r9Kd1vPBj7wgRkdlyM+d282Hygqkm3rMkXAAB4pdgeorRVlu+qQFFJbcLbK5qzOfr5xud5z162N+F9tlVIbCYIgiAIgiAIgiAIgoiAxImkgRhFuQZvQOdwZAhIXHQ9UutR+yMr+PUJfXTLrDKb4+GRC0bqXnfItM6nZvvjxXerIoVMEG4OsZnX/i62yJeOB9ZFh12Eyy5qTnajEP+HaQOT3leq8BjEZmPkBy82VzfpRXLmErc1l0U8TbjqjVWY8cKyhLfXMpvjPEyr91aFucnbOyQ2EwRBEARBEARBEARBRICPfwhIClbsroTHHzmzV5IVUwEvGWczc6GeOLAQj104CvuenIFOQfdxKpzNVx2vF7DtNmvZSBAEiIKa+cuwErzZcUhVjIbPwtn8zGVjzVaPCyaIO20iXA4RS4vLsXBLqe44PnjucPxiRLek95UqNh6swZFaNwJB4TgsRiPC5xiQ1WOZiocV7RnN2ZzAtgeqmrB2f3VK+5POkNhMEARBEARBEARBEAQRAd6RW1xWjyv/vRKzviiKuI0kK7rh9Ay+6Fy8uH2qMMjnJLuCrlUmFvbtpM/zFZIqERgZo0BpJXhrMRopMnj6A6Hjl+oYDfbZ2IMxGuX1Xtz83lrUuQPcPlO6y5Rw//+2wC9bOJsjiM3M2RxLPMSxjJbZHONhGtUzX5u+4MVluOSV5Sl72JLukNhMEARBEARBEARBEMcwtU1+7K1obO1upDUBTiSqavQBALYeqYu4jaKYi5KCICBRzYkVBeTFZokTRwHg+79Mw7Un9uX2l9i+9jzxy6jrGMVmq/fFtM/myWxOSZMaWoyGTdTFT/g5pTwdXMD7npyBoofP0jKzj9R6Qs5mg7gsigL2/u2XmHnO0LB2dpXVA2jfMRrRRN6qRh9q3ZG/BxXt3I3tOH16yxRtutGnXrfeQOTREO0FEpsJgiAIgiAIgiAI4hjmhnd+xmlPf9/a3Uhr+AKBTHiOJpxKimIqSooCL1zFB4vuyHCE5JyjdWqMhS0YnSAIgk5cS0RCzHHZTV3ZRoyu4m+3l5mvF2xLagaxOdXGbXbonDZR1zQTcoH0cQFnu+w4Kxjn0TnXxcVohPdPEARkOW1h83/ep8Y7WOVttwe+3FQScfn4RxdjzMNf47Snv8fyXRWm67BPP9bnDPyDCobHf2xkN5PYTBAEQRAEQRAEQRDHKN6ApGWJRnP/SbJyzAwDN8K/b1ZgLVokhKwophEPoiAk7PBl/TDLUub3dOG4UKG8eIXRjQ/9Aivvmx7TutFyqxlagcBmyGx2RIiISAT2IMBhE3WfE18YkneWtzazzh8BQC3myNzXZkInAGRE6Lc3xs+yrVHv8WNrSWgUgj9Kgc/tpfWm89l5Ec/19OJV43SveWezLyCn7HpIN0hsJgiCIAiCIAiCIIhjEG9AwpAHFmqvL375p4jrD37gK5zxzA/N3a20hBebNx6sARDdCSrL5mKzAOu4iWgwV7VZ5AErHggA2U67Nh1v4kN+lgM5Lnv0FRH7+2AO71Rpa37OZWyVE50ofGbzpkO12nwft08zh3BrkeGwYWDnbEiyYlkgkOGyEKEB4IVvdzVL/1oTWVYw/tHFeG3pHm3eoPu/irjN2gPmhfwSKRDYq0Cfn86czYqiYPADX+HBz7fE0VrbgcRmgiAIgiAIgiAIgjgGqW70615v5IQ1MyRZOWaznc3iHwYUZkfcRlbMs30FQUg4RoOJ3ryWeMu0gQD0udJ8gbiWiHz497UTIy5nhyFlMRqBkDvVKKz+NPN0/HDXtITbZj00CvpuTsxPJ2czoJ5nkqxorl2rByHj+xRo0zedMqBF+taaBGRF92AiFqw+W01sjuNyMor7zNnMstffX3Ugrr61FUhsJgiCIAiCIAiCIIhjkBq3L6HtTv3HdynuSfoTMBGs3FFiByRZMXUVi4KAWDTXwfd/hdeX7tbN01y3nMDav5MqevNtmsVsNAdMmOua54q4XqpjNBYWlWrTRvGvZ4dM9O0U+UFAJHoH3ahGwZYVeQMAlyO95DSbKEJSFC0CwsxRDwBduM9pZM/8Fulba2IV+/N/H2/QpvvNnK9bVt1o/r0YKg8Yu9psFJuZs3nDgRrd/kfNWoRD1U0xt5vupNfVQRAEQRAEQRAEQRBEi+D2JZbRur+y/YgisVLvUV2t04Z01uY1RTl+1pnN0YsLun0SfJKMJxZs183XYjQ4NYeJonyLqY6WsIJlAztsIt678Xh8/5dppusxh3cqMr+NgnWqjdtvXT8Jr/56PHIzHPj2zlNx55mDw9ZJdU50sthE9dj6go7vQV1yTNdz2UOuXbMolvZGwCJYfe66wwDMM8c9AfPrOpTZHPv+XQaXNNtfSa1HN7/eE8D2I+ZZ0W2R9Lo6CIIgCIIgCIIgCIJoEXyBKBXuOPYdo/EZjOom1e149ohu2rwfiysiFhuzEpt3HK1Ho09CozdgspXKrC+KAAC5GfrsZCa08u1qLmaFj9FoGbmHic1Ou4iTBxWin0W0SCizOXmxeY/hXIzHaRoLhTkunD2yOwBgQOccnDumR9g6Vs7h1sImCEGxWRUz8zMdUbfhn0ewjO5oxfPaGpEebiiKgpomf9h8q9iNRM5co7P5xWAu9vc7ysLWXbmnMoE9pCckNhMEQRAEQRAEQRDEMUg8WabfbA8XR44lmCNxSLdc3Hhyf21+SY3bchurzOYfiysAAOu5ofRGNgSLEJ46uLNuPhPP+BgNh2jibI5SvDBVOIOidjQnNcuNToXYfNAQN9Dcum/fjllh88w+19YklNmsHl9HpEKAV47DB787HtlcEcjMYMHD9iY2Bwxi84xR3bVpb0A2jcIJWByDVGQ2L9ulXvupuA7SGRKbCYIgCIIgCIIgCOIYxCfFHqORCmntpe924ZrZqzBnxb4UtJY4Hr+E+/63GZUN3pi3YSKvwybiwXOHa/NZvIYZsqxEFKYyIuT+skJ6XXIzTPvB18QTmdjM6VcOsWXkHiamRRMpWWRDslpmVaMPN727RjevuV3GoomwnG4RFOsP1mDZrgr4ggfYGcHZfv6YHpgysBAds53aPLZ+vMX00pkNB2vwz6936Oa9dPV4PDBjGADg93PWot4T7mw2CtQhgjEacWU2hxcbXFRUahphlKrimekAic0EQRAEQRAEQRAEcQwST4yGUVtTEhBG3lu5Hz8WV2DOyv1xb5tKFhWV4oNVB8LykCPB9CcmbP5uqupujig2K4qpA/aOMwYBiCRqhURlowOSCVJ8uxVB0ZzfVUs5m1+9ZgIum9gL/QvNM4IZTPtMNrN56c5yTRB9/ZoJuGR8LxxnkU/cnLSQlh8z7DTxBp26jhg+/4GdQ8eNXd9Wrt62yIUv/YQPVx8EAGQ5bfj0lim65T/sLDfNSTYrBgok5mw2+xx+P2etqaM6FXnm6UKaXR4EQRAEQRAEQRAEQaSaqkYf3vppryYS7zxaj5vfWxfz9kbJ5Psd5XH3gYmrO482YN2B6ri3T5af91Vh6c5yZDnV+IBP1x2KeVsm+jKR8ZfB4fhmBcYA1em7r7LJ1PHLojHMBCcGK2zGi/o/Fpdrua682MwE78n9O2rzWkpsHtw1F09dOiZqrESqYjR4gX5M7w7452XR951KmLCdbjEajMpGH5w2UTvekch02vD7UwcA4MTmdiR48jx6wUhM6FsAAGjgstLv/nQTAODicT21eVZFBdmRiUdstvocVu6pCptHYjNBEARBEARBEARBEG2Guz/ZiIfnbcWWw3UAgKcX7YiyhR6jaHL92z/H3YeAJKNDllq4bPayvXFvnyy/enUFrn1zNbKdoaHtsTq0mUjK4hNYxq2VYPy/9YcBAAs2l4YtY0X1vH5rF6kUdFfyQ+uvmb0an28o0fUDUHNoO2Y7cdXxfbV5LRWjESu2FInNMifIFWQ5I6yZWi6f2BvXnRg6vulWIJBdV4eq3dq5GQvsc2HREO0ls7nBUHyTf/hiVpize4dQXE1UZ3OKC1Iy2lOOc3p9+xAEQRAEQRAEQRAEkXIqG30AoGW6xutMToUQEpAVXDi2Jyb0LcDXRaUJRXGkAt5A6ItRXGOuQya6ZzqCYrNPQkmNG795azUWbjkSU1usuF+kYxrQYjTMl/PO2t4ds7DuwTPRvzBbm2eWM9yasP4m697kxX1nhCJ4qebvl47GwxeM1M7Z9Dq6wLOXjQWgRkMUBIXnWGCFHUMxGu1D8KwOft8x+OvFKEQDqkOf4bd0NgdHNzTTh99ejj1AYjNBEARBEARBEARBHHPEKrIyUuF4lGRFE7f8koKSWk/SbSYCLyZ5fLG9L6YLM9GKFf7yBmRMefJbfL+jXBdL0jHouj1xQKewtlh+cSyZzVaCPIsCaSsw8TvZpIBGnyoUnjGsa7JdalcUcMX+4inyZws++GD6qVWERFujyiA28wUTZ4zqEbZ+bkboevJYjDhg525zmdqpQCBBEARBEARBEARBEG2C8novioLxGbVuH/ZWNGrLcjPs2PfkDNx4cn9kO23YXd6AJl+48y8eAcuKgKzAZhNw9fF91DbjKFCYSg5UNmnTaw9UxeSwZuIvczWylIp9lY2m6zNR+u6zh4QtYxEMcgTlVXM2mxyinh0yk3L1bijd0OKucnbcknXIu32qs/n1ayYk26WESCS3tyXoyEWKXH9Sv5i3Y/ESzLGfius8HTCKzR05Mf7kQYXY9+QMXMTlNGc6QmJzTZPP9PoIzUvsw3/7+kkRl0f6PmhrkNhMEARBEARBEARBEO2YSY8v0ZzMN7y9Bqc9/b22jMkmNlGAX1Yw/Z8/4BaTwoGpcjY7RFETW1tLWvnrF0Xa9A1vr4kpP1orEBjsO/v/9aV79OsFBSMmFjts4bILi9GIFCnBlpm5HV1JCM3f7/se414bhxdXv5hwG4nAsoGTjdFo9ErIcdlbLyZE6356qc2dc13adDwPIoYE4yNG9swH0H6iHGrdft1r3vnNOG1oF23awWU6+yUF9SZRG4x4HzQM7KzG2/TumBVxvfZUnJHEZoIgCIIgCIIgCII4xrGJAnxBp/GPxfo8Z1lWsKusIan2FUWBJCuwiYIm1rRkQaxITt7Nh2u1aUlWUOfxh62jic1BkdNmoTh5AqrzNhAU520moihzRUcSXlmcgdkx4oudxUtxZTEAYOPRjQm3kQhajEbSmc2BuArgpZp0dTZnOm345ahuAKBdx7EwfVgXLJ95Oi4OunzbS4yG8Rh0NCkmef6YUJyGTRSwedYv8PdLRgEIz3wG+AKB8TH/tqkoevgsDOycg/UPnom1D5yB7/8yDZP6FejWowKBBEEQBEEQBEEQBEG0eaYMLAQAeLjCa0beWLYHX26KrfidFUxYtYuCNmS/JaMcIonlvOg789NNGD3r6zBRlL20GZzNRljMQ8jZHL6eViwvUoFAiWU2q6958Ys5o6NhEwVdfIDaL9WxaRNaVrC1pSqz2SshuxXFZkaaac0AgIGdcwCYF8CzQhAE9OiQqT3ASNZ5ni6wXPZBXdRjkp9pXjQxx6XGZzhsInIzHJpDvLop/IETKxAoxPmkIcNhQ3ZwPwXZTnTKcaFfYTYEw1nUXlzlANC2EuUJgiAIgiAIgiAIgkgJT1w0ChePVx2Nw7rlafONkseecvNc4nhg4qvNJmj5vS1p5Ctv8Fou4x2F/117CIAqBIucGGRA+80yAAAgAElEQVTMbBYs9F6WecscojYTYVgTmy2EPb8khzKbg32r4WIBzARsMxbdMVUrZMiQFCnYh5YVbNlxi6cIWkCSYTfEkNR7/MhsxeKILZ11HQ+xxLNYwc7J9hLlwITbN38zCT5JtoxdYZ8nE6NZdrPZw7dEnc2WGBoiZzNBEARBEARBEARBEG2aod1zkeFQRcc8zvln1DzMoiAAYMnWozHvi3c2awXyWlBbYY7jUwZ3tuwbLyQaBbsHPtsCIBQHYeVsZtnWTOyymxy7aPnFvNDF1uFzto0CrBXHdckNy4lddmAZAMBlc5lt0mzEUhSR57Evt2LS40u0OBIA+OfXO/DdjnLTY9pSMPdwa0Z5WKEJ+glcWMkI1ekIuw7zMhzaZ2bGiB5qVjXLdHY51OPgNYki0cTmZjr92ovQD5CzmSAIgiAIgiAIgiCOSRyc6zaSgMcvu+aEvpizcj8A4LsdZThjeNeY9qU5m8WQX7glnXyNQbE51xUugzA9kxfarLoWKhBovpy9T/a/Wb5yNGezmxObzfqRjNia5VDF5975vRNuIxGivWcjbwSLNvo4d/O/vt2lzosjkzjVPHvFWKzdX43u+Zmt1gcrxBjiWaxod87mCNcfz2vXTMCmw7VanAYrvuk1czYH/zfGXyQKa+U3U/phy+HasKKGbRlyNhMEQRAEQRAEQRBEG+OHneWY/s/vcbTOE3E9v2QtzPGOZVsEUYaPgrgoGLsBWLt7zTDPbI5586TwBWTc9uF6AKGMVh4mevu4Y2Ul2EXLbGZO3JC4HrvYvLWkDhMfW4KDVW5t3vzN4VnZFREiQayQFRknvXkS3trwFgDg1TWvxt1GMoTc7PF96OwYNflCOcSJiKmpIi/DgdOGdGm1/UdCTOK6smvnZPsoEMiuw2hic0G2E6dyox00sTkgY29FI8Y+8jUOVjUBCI18SJWzmbVzxrCu6JqfgfUHarD+QHVqGm9lSGwmCIIgCIIgCIIgiDbGm8v2Ynd5I7aX1kdcr8lnXfiPF6IjOps5wWZc7w7adDyiSyjDOJTZ3FLO5sM1IfE2N8PM2RwUmznHrJUDl2U1G8XmzGAciZbZHDy2jkiZzYb3/87yfaho8GLexhLrNwNgdwIZ2m6/G8sPLuf60NKZzYmJzUz73F0Wes/tKds2lbB0lVijSvTbBp3N7aRIHbsOza6/SLCMc49fwn9+PoiaJj/mbVKvx1Qfmaxg9rjbL+HcUd0BADuifJ+3FUhsJgiCIAiCIAiCIIg2BhOH3b4AbnlvLb7fUWa6Hu8INcI7eXkHbmGOdZ6vIAgoyFLznePR/FrL2SzLCs56bqn2OicoNvMOZzNn858+WAe3T8LbP+3F/9Yf0rUHhMdoXDFZjaVgojp7v2aOcXasn/xquzbP45fwnzUHAQDLdlVEfE99DDnMsRCQ9edBk78p7jaSIeTmjm87Jsg77SH5irRmcxwxZnmbwR4otYfM5lV7KvH8N8UAYFkY0ArmbN54qAav/rAbAFDV4MPUp77F3Z9sSqhNKzoEv0ebfAFM6FcAoP3EmFBmM0EQBEEQBEEQBEG0MZiwdLDKja+2lGL9gRqsvG962HqNXmtnM+9StnMOwIBhKD0ToJ6/YiwAbrh+HF4/5pjknc3xbJ8oDb6AzrHMCiLmuOy4eHxPvLtiPzrnquI6v96PxRX4assRzJq3VddefrCQIu9sPq5LDsYGj6XmbJatnZVOThT0BiS47Dbdw4KaJp9ufcWgrnYMFjOLB0lRz4NT+56KFYdWtILYrP6faIwG765vrgJtbZ0rJvXBzqMNuPX0QXFva29Hmc2Xv74y4W2Zs/m9lQe0eUuLy3XRNqk6/e7/5TC47CLOGtFNG4HSHsR+gJzNBEEQBEEQBEEQBJG2VDZ4MeuLIpRx2cwr91RiYVEpANWBBwCldZ4wURKI7Gy2c6In72w2DqWXZAW5GXZcMFbNaxa0GIzY3wcvGoYiFWLfPlGMkQIsc9kmCnjkgpHoVZCpjY83Fp57fP42bbpHfgYundBLc2Xz7sa3r5+kucG1zGYpFBtiRODU0j3BSAx+XkWDKjafGSy+6DPYgRMxVjJn82UjLsNtk29DRVNFmNu5ORESjdEIrs9v5kzCwdueyXTa8LeLRyE/6JiNB5bL3l7ETgDo2yn+EQAuR/i5tfNog+51qh52dMpx4W8Xj0aGw9buCjTSFUoQBEEQBEEQBEEQacrnG0rw9vJ9eH9VyGl31ycbtelat1+bLqsPLxzntshsfuKiUbrXfGazsaigrCg60fTZy1WHM3P5xkKoYJ6oWQNbInvXKJ55A5Ju306bqIm5RlG3sjHkMPZJimVMQZbTHlb0L8DFhpgxuX9HAMDuclXIMis42CnoYObd6SN75uGh80aYthkJJizbRTuyHKoIV1RWFHc7icJE/njFTLY+f648cfEoq9WJBGlPzmaGx289qsOKWB5kCCnzNodobwUakxKbBUHoIAjCJ4IgbBcEYZsgCCcKgtBREITFgiAUB/8vSFVnCYIgCIIgCIIgCOJYgkVaNHpDLlR+SDc/30wo8psU/Mpx2XHV8X1083TOZjnc2WzjxNCpgzpDFIAvN0UuZGdsA1BFFS2GoyXEZsM+2Hv71YReAFSXIlvFH7Duj1+S4TTJXwaALKeNixYJ7kdSIArW+a5PXzoGAPDeyv3w+CVTt3KnHFVs3luhup8fPHc4vrx1qhbZEQ+SrApvNsGG0/ufDgCY+c1MvLvxXXgCnkibpgSjGB8J/ryQFQWKouDt5fsAAK/+ejzG94lfZvqq+Cs8uexJNPgaoq98DGJrZ2InEH8+OBBbHnNzxLiQs1nP8wAWKooyFMAYANsAzATwjaIogwB8E3xNEARBEARBEARBEEScaMPbLYRZ3vVq5mLmXcrMTXv/jGFh62Vww8clWdFFSsiKPuaBzTtY5Uadx49YYKK5LrO5RWI09K8l3mEd7E+oQKC1E9IvyboidTwuu6i9J9ZWQFZ0MSVGMpzqspV7qvDs4p2mzuaO2Wo0x/JgwcBOCWQ1M3hn85DCIQCAhbsW4rrPrsOD3z6YcLuxwkS8WD5zXm+TZeBIrQcfBJ39ZscpFi746ALc+829WLhrYULbt3fao7O5uYTz5oga0ZzNJg8H2yIJi82CIOQDOAXAbABQFMWnKEoNgAsAvBNc7R0AFybbSYIgCIIgiPaK2ydh48Ga1u4GQRAEkaYwMy3LHjZGXDRwzuYjtW5sOFiDH4vLNTcsW//LW0/Gx78/EfuenIErJ+tdzQBQkKUXMvkidbKswKibPnXJaABAvSe23F/e2cyGobeErmXlbGYF50RBgCQrWLL1KDYfqrVsxy/JljEagiBwmcTB/UiyZYQGAGQGCxUCqphqFilSGHQ2l9SqTnb2sCAReLG5W0433bIqd1XC7cYKOxRWD014+GMhKYpWPA3QFwqMFUVR4JfVhyJf7PgCiqJg3o55eHfju9h8dHPc7bVH4nGetxUSfS+zzhsecXki8RzRaG/OZnsS2/YHUA7gLUEQxgBYC+B2AF0VRTkSXKcUQFezjQVBuAnATQDQp0/4HzqCIAiCIIhjgb/8dyPmbz6CtQ+cgU7B4kIEQRAEwdBEoKAAt2J3pW55I1cA8JrZq7Xp3h0z8ePdp2sxGlZCKaNDUGzukZ+BkloPatx+dMnL0PZtMzhKs12qnNAQo9jM+sE7m1sis9lYIDDkbFY7IQgCissa8Nt310Rsxx8hsxmAubM5gtic5QzJMaJgHnfSMehkPlTt1r1OBEkJxmiItrBlLnvz33/Ek9nMnxaSrGg520BizuYNpRu06Tmb5uCek+7B+R+dDwAY0XkEtvxhS9xttjfsQae/sThoW+asEd2ir2TCiJ75EZcbH8ylAkEQYBOFdiP2JxOjYQcwHsAriqKMA9AIQ2SGogbtmB4pRVFeVxRloqIoEzt37pxENwiCIIj2QK3br3PmEMSxwoagq7nJooATQRAE0XzIsoLio/Utkh2cKKLBcdhouF8yvmawXGfmbHZEcYTaRAE7Hjsbj1wwEoA+kkOWlbAs02yXKlrGev/G+uG0i5oLuCUOu1G84R3WgCr0Hql1h21nhlWMhtqOPofaG5DhtIcLuww+I9tpF8Mc6wCQl6EWYCwqqUOGQ0SGw7q9aPDOZgDwPhAqJplhz0i43VgJxWjE52yWFQVeLtKFiaLxwMTms487GwCwtXwrAGBY4TAUlbdckcR0xmZrH85mFutz8fieeDI4+iJeJvXriDk3TtbNG9MrH1sfOQvbHz0bBUk89ImErCio5kaUtGWSEZsPATikKMqq4OtPoIrPRwVB6A4Awf/LkusiQRAEcSww5uGvMfnxJa3dDYJocZqjyAhBEAQRGx+vOYgzn12K73ak/89WJgL5DKKkmSOWx6eJzdF//rvsNmQFRWQ3N1Tcb+LSzc1QRUsrsTusH0HB0GkTuczmFnA2c/vo2SETAzpnAwB6FWQBUEVfjz+2bFczwX50L9UFycRmFhPrDUhwRRCnAWD60C7atmZic5ZT/SyqGn1wJCCy8jCx2SaobTptTvTKU4skdsxMPJ4jVsRknM3c5xND/bYw7l5yNwDg1L6nAgB2VO4AAO3976vZF3+j7Yz2ktl831w1FmVkj3zdA514MY4i2HioFllOe1IPfKKhKMDmw9ZRPm2JhGM0FEUpFQThoCAIQxRF2QFgOoCtwX/XAXgy+P/nKekpQRAE0e4hZydxLMLE5rZ+c08QBNEWYT/smQs4HWHiHNMio4nLRtiw+EiuXB6WJcyLzTVNPi1mg6HFaMQrNttFTdytdcdWXDAZmNj80LnDcdmk3siwixjdswNG9MgDEF74EADe+s0kZDptCEgKnluyE2v2VwMIF+xX3jsdeZn2YDv6/fkCclSx+aWrx2PMw1+j3hswF5tdIclmWLC/iSLJ6ufJnM0A8Nnln2Hivyeid17vpNqOBS1GI4bTV+EGyBtjNGLJfDYiKzJO6n0ShhYOBRByOp83+Dws3rMYlU2V6NehX9zttidCmc3NU1SvpajzBCAKwNUnJBfXm4xQnSi9CjKRn+lo8f02B8k9GgNuBfC+IAibAIwF8ARUkflMQRCKAZwRfE0QBEEQMcFXPieIYwFWJInOfYIgiJbF7ZPw/qoDANL7OzgkNqt9DJiIklZsLanjYjRi+/mfFxQ7fiqu0ObVNPnRIUsvgnTIVMXnb7fH5gr3cTEaTMe55f11WHegOqbtE4Udrm75Gchx2WG3iRjVK1+LdTDTlAZ1zcEJAzrh5EGFmNgv5Po1HsNu+Rla9rJoKBCoxmhEPuYZDhucNhHzNx3Bl5uOhC3P4lyUAwqzI7/RKBhjNACgMKsQQCjPuTlhxuxHv9wadV3++XtRSa3u+kzEWdrga8BJvU9ClkN1s3+67VMAQP+C/gAAn9Q+oguSgT0MaOvmB1lWMK5PAVwRImxiwZhR3zWv+XPNu+ZltEiOfUuQTIFAKIqyAcBEk0XTk2mXIAiCOHapavShW37z58YRRLph5mgiCIIgmo+yeo827fGn7+gqTWwOahDx/L0orXNr69ujZDYzmKjJO/u8AQkZBvGG3a/Fqo3wojfvJi6r81htkhLY8bMyKpoVnONFYj4+xBmpQGBwkRKHsxkA6oPO8B85cR8A+nXK0iJNACDHlZR8E4rR4AoEsmlZaf57kHicorzgFpBDmc13njkY4/sUxLVfn+SDT/Ihz5WHTHumblm2Qz3XvZLXbNNjCjFYuLOtZzYHZDlMKE4E/nx96/pJGNkjctHAVCAKoRietk6yzmaCIAiCSClVjaqz4Pq3VuP2j9a3cm8IovlhP2KfW1Lcyj0hCII4tvjjB+u06XR+4MfEn3kbS1DR4IXPJIfAmKfM+GZbGR6bvw1AZKGURxAEdMx24kBVE/rNnI9+M+dj59EGU7F6RI881MRY0GrV3ioAqjOV14Ka20nJhEszURkIdzACgMsWEmR1hREjaFjhzmYpJndltzxzk8UJAzrBaRO1Y5WdoNh8z+J70POZnqjxqAWJeWezKIjBPqeX2Mw/wHD7JDyxQD2HLxzXM+54g3pvPQAg15WLHGeONr9rdle47Kpb9dU1r6Lg7wVa1Mixil0U447pSTdkOfTgJxnYeea0izhtSBd0zm1+Z7MoCO3G2UxiM0EQBJFWNPlU18V3O8rx+YaSVu4NQTQ/Y3p3AACU1zevs4sgCILQc7g6lNPsTWexmRMfDlY1acL4nWcOxvljegBQBds5N07W1rtgrDqfxYQAscdoAGpu87Jdeqet2fYds52oilFsdgTFmx75GTrht7mdlKx9K5HSTIN22EMzeTFajtBX1jyf2RxLTvYrvx6v68dbv5mEv108Cg+eOxyCIGiFAVlBxnh5avlTKKkvQXGV+lC7tcTmeGIN+MKRHr+EI7XqPZLLEb+EVeetAwDkOnMxuutoPHH6E7ho6EWYd+U8OG1qFMx/iv6DGk+NJsgfq+Rm2FHvaf4c9eZEUhTYU6A2s++olkxuFgUh5pEi6Q6JzQRBEESrw99Q7iprwMD7FrRibwii5fh8w2H8b/1hAEBlo/7H+sItpbhm9qq0zhElCIJoy/Dfr+n8XfvUwh3atNsvaZnNN08bqInKADA2+PASAH5/ysCwduJxhGa7bKj36Av/mbmnC7KcWH+gBpUNXny5qQSXvLIc5fXmkQQ+SUZhjguCIOgiLQLN7KRkYr1o8f5NYzQ4YZ13dEfqqaA5m9W1vDHGaIzrUwCbGBKZpg3pjCsn99GczCxKI1FnM+PPi/4MALAJnGs7KDa3tqO30RvA5a+t0BzMvKbPFxCPNx5BVmSc/u7pAFRns0204d6p92Lu5XMxqeckuGx6t2qPZ3pg4usTsaVsS4LvpG3T6Avg/VUHWqRwpxXegISr/r0Sf/08sc8gICuW13o8sOu+R4fMKGumDlEEOZsJgiAIIlXwwye/2FjS5rPCCCJWnueiM4x5oY9+uRU/FlegpMZt3IwgCIJIEkVR4Oa+d9NZbObx+CUtRsMuChjYORQLwLtoHTHmM1th5kK1mzibB3dV97/jaD2eWbwTa/dXo7is3rRNX0DRxNcsZ6j95r7vY2K2w8LtaDabf698XyPBRGumFcXqbAZCIqrTkGcNAPedMwznj+mBqYMKY2rLyJiuY3SveWczE55bwtkcid3lDVi1twqvL90DQG9E8XLXZsdsZ1ztVjRVYF/NPnXbzI5hy4cUDtG99kk+rD2yFssOLItrP+0Fj1891ouKSlutD0drvVi+uxLvrNif0PayrFjGCsVD19wM/GZKPzz9q9FJtxUrFKNBEARBECmEv4lcvruyFXtCEC0LfztpzKxkP743Hjq2h3QSBEE0Bz5J1rkn0zmzmWfzoTr4JRkOmwBBENCnY5a2jBdT4821NWK2vZmAfcrgzgCAJq+kHUOjU3nhllJ8sbEEPikkvuZlOLTlqchsnr1sL9burzJdxh4kWAm/VlnODF7gjKQDGWM0YnU2A6HjbXaML5vUGy9cOQ69CrLClsUCi4pgxBOj8dRPT2FNyZqE9hsJxXAgq7jRXYqi6K7N2cv2AgBmnTc8TIiPxgUfXaBNF2aFi/VOmxN3TbkrbP6i3Yu06e/2foeXf345rv22df7z88FW27c/gQp5Hr+EQfcvwMItpZBkJeo1HQuiKGDW+SMwoW/4Q4rmQhAEtBfPFYnNBEEQRKvjTeMK8ATRnPC/5SXDj/NeBeqwvYqG2LIwCYIgiNgxRkSkq7PZmBHc5A/AH5C1/GR+uDg/bcxXZtnOsWImNpvloGY5VeGy0RcA04gCBrHo5vfW4rYP18MXkLR4isIcl+bUlRIQl4w8+uVWXPLKCtNlTAS3cnszYcpKGC7I4sTmCEEarB32sDguZzMTm2NcPx78sj4SwSaGx2hYic33LLkHk/49KWV96ZqnxlYYRfsGb+h69EtKmBgNAMcP6BT3/lYeWglAfZ+DOg4yXSfXmatNv3buawBCRQUB4PR3T8cfF/wx7n23Ra4+vg8AYO3+6lbrA/+wyuw8MOOLDSXwSwpufm8tZEVBHPH0aYUoxP6e0502+hEQBEEQ7QljVi1BtBX2VjTi0leWaxEYB6ua8PmGw1G3m7/pCB6fvxW7yxu1eUZnV0HQSRVoI247giCItkTx0Qbda1+aftfy/cpx2bH5UC2WbDsatdifUSx+4cpxce3XLBvXTKzNDuYJP7VwhzZSzW+RweyXFK3wnigKeOEKtU+bD9fG1Tcj0R4UeKM4mw8H46pmjO5uujzW6AZ2yJhW5A1IMRfFY2JrvJnEANDkb8JD3z2Eh757CG5/ePSWX9KLzWbOZkkJN34E5JAAXN5YHne/zLj6+L4AwnNp+c9QVswl/WHd8yK2PWfjHMz4YAa8gfDM8MXXLEamwzx7d0SXEQCA8wafh5sm3ITzBp+HzWWb0e+5fvhmzzfaeh9s/iDi/tsDl03srU3XNrVObjM/yoTldZfXe/HR6gNWm+gKegbk1BQIbA1EcjYTBEEQROrYXmqe7UcQ6c5pT3+PNfur8a9v1ezlK15fids/2hD1h+8fP1iHf/+4VzfPKrMyFcOLCYIgCD37K9WHfQM7ZwNQ84TTEV54sdsELN9diX2VTZZi86AuObjn7KG6onaJYKbVuE1GojFn8+EaNyoaVJHPquBfozeALAeXFxzs48drDiXVVxazYAUT7K2cy7vK1AcPOS47RvfKx5/PGKxb3qdTKL7itCFdLPcjGgoExuNsZiRiwFh5aCUeXfooHl36KJbuXxq23C/70Te/r/Y61hgNXrT9cueXcffLDHZWGs8Q/r4pICsJ5dZe+9m1WFC8AM+tfC5s2fju4y23m9pnKnKcObjnpHsAAKf2PRX13nrsr92PM+acoa1361e3xt2ntgZ/vt7/2eZW6QN/37u9tA4AcNuH6zFz7mYcrGoy3aZDZuiBkJSiAoGtgShQgUCCIAiCSBmtWfGYIFJBWZ36g6w8+EP7jWV7tB/dRqyGxxmHHbPV2kqOKEEQRFuC6Rkf/O4EjO3dIW2dzW5fSODlBWYrY/Pi/zsVt0wbmLSzzyxGY1TP/LB5ZsXzjH/PGDVNfhRkh7KaE3HxMtYdqEZFgxc/FpejvN787y1ja4kqWEVzg3v9Mr7408m4/YxQ3MKakjX46/d341/Xy9j35Azsr9+AssYy0+3ZMZMVoLTWg0afFHNmczLwbuY31r+Bh79/GDUetd5DvbceOyt3Ymrfqci0q85eVhRQ7bM6PWfTHLj9bizatQhvrHsDTf4meAIebb1/rvgnXlz9IraWb02qr0wENN4K8fc6kqQk5e78ZNsn+G/RfzVn9iPTHkGHjA6W63fO7oz6e+txUp+TAAB3TrkT866cp1tnSKchqHJXYfa62ShtaL3iec0Nf42U1noirNl88CP6Gr0SPlt/WCs6uu6AGu+xak8lth2pC23EfZXsrQiNGmxrUGYzQRAEQaSQuqDYfDk3dIsg2hL9ClVnXKYjNJz44XnmP8isnMqyos/mZM4GK4cYQRAEkThS8DtWEFQ3ny+QnvUjnl2yU5t2ckJQJZfn3y0vA8MNEQO8s3lQl5y492uLUaw2E3CtYjTK6j26SIpkhNiLX16OiY8twTWzV+PNnyI7m1nhwAKLOAymq4/sFS6mn/r2qXhu1XM4/6Pz0ehrxJQ3p2DK7Cmm7TDtXFYU3PPpJgBAdQtEEfik0LnwydZPMOuHWfj32n8DAD4u+hgA0CmzE+6beh8ECLpCeczZvKVsC95Y9wbOfv9s/G7e7zBvxzyd2FxUXoRbv7oVdy++OyV9Nro3+WLhflmOO7eWX39NyRpc9sll2FmpXjtZjvgLKw7vPFz3elJPNbf6t/N+i+PfOD7u9toKmdzDo/xMR4Q1mw/++2NRUSnu+M8GrX7Juyv2AwAuf30lrnh9pbae8V553saSFuhp6qHMZoIgCIJIEbvK6rE7OHzxbxeP0uZfMr4XenbIxOEaNxq9AavNCaJVGdpNLSrTKdsJt0/SOXMWb1WdL7vKGlDd6MPy3RUISDLW7NMXXZn7hyn4vzPVIbsSd4PJ2kqkKjdBEERzoygKdpW13Rgs9oPeJghw2cW0LRDIsv0n9++oE+hOGdxZm15533QsuH2qbjs750x+8SrrCAErmFb96q8nxF1csKZJFYYqG7y6UT7VTX5dsT27TcTt01UXsbEQYiJkm7isAXVY/aR+BcjLMBfP9vxtBrY+chauOaFv2LImf2jY/u7q3dr/ZoIQi9HYXd6AH3aqGcf1nuYVm3dU7ND6uPamtdp8d8CNnw78hLnb5wIAnjzjSTxwygNour8JBZkFXJ9DktALq1/Qpssay1BUXqTb14m9TkSDT591Hi9WZnZ+ZIEkK/D447sey5vU433PSffgvYveAwBsK98GIDGxuXtud9w15S7t9Q1jb8CU3upDhgO11tnBbZ2CrNA1ktcKYrOiKJi/OSQU7ynXu5QlWdGcz2xkbFWjD6W14VnlbRE1s7l9iM326KsQBEEQRPNxxjNqtpzDJmhD6yb1K4BdFCDJCk568luM7pWPL/50cmt2kyBMYT+OfJKMxxds1QqZAIDHL2NrSR1++cKP2rxZ5w3HLIPjOctp05xhkqwgaI7WxGZyNhMEkY68v+oAHvhsCz7+/YmY3L9ja3cnblhOvigIcNjEtI3R6NcpC6v3VuG0IV3w94XbtfkZjsi+MT5GwywSIxq8s3nqoEJ8sbEEQ4IPWKOxJVjwb8JjS8KWGaPTXMH34ZNkZIixFdOzqnEgWCiZbr+Mnh3Mi8MxWPY0j1FQnrttrja96egmjOk2RrecjW5666d92rzRJm7pSEwb0jn6SkEO1x3G0JeGomOmev11yOiAE3qdgJWHVmLV4VX46/d/1dbNsGfo/mfwYvOuql3a9G0LbwvbX44zB/W+5B4wCeIKfwkAACAASURBVDCP0eDF5YCs4M6PN8TV7mNLHwOgxl0wV/L84vkAEhObAf2x6prTFaO7jMbyg8sTbq8twF8H7KFRS/LpusN4b2VIzF+1t1K33BuQUe/Rm5DGP7o4rJ0xcV536QIVCCQIgiCIFMOGhq66bzreun4ybDYB3uCQ1k2HaiHJCsrqWic7jCCsYBEwTT4Jy3dVhi3ffLhG93rH0XBHkF0UNQcaP4yU/fBq8JCznyCI9GPTIfX7bV+leT6mxy+hKsZiZ02+lv+eYz/oRVGA0ybCn6YFAnt2UIWt303tr5sfLeaCdzbbExKb1f9lRcGlE3ph9X3TMbqXde4tACz+8ynIddkhigIqLeoWGMVmdv8Xj9hvVctAEFQh+mBVk04o9vglXTxArPAxEgCwuSxUMM0stznbZcf4PvpjdOPJA+La58tXR3ahu/1uVDZVQpIl7KjcAQCocqsxIS6bCwuvXggA2Fi6UdvmkmGXWLbHi83RcNqcusiORGCnomIoEVjLCZuSpGDjodq42t1bsxd20Y5rx1yLsd3GAgg5kBMVh88aeJY2PbzzcDxz1jMAgIuHXZxQe22F9Q+eiTG9O+juSVON1Xf+V5uP6F7zwmuvgkx4A1JM/XqCGy3blhCoQCBBEARBJA8/ZJK5UbrmZSDHZYddFHQ5d49+uRWTn/hGK/JCEK2NX5K1DLmlweGyRu75VF/J+6jJAxOHTdCyNW+eExoC6/GrD1uW76lISX8JgiBakl+/sQrjH12sK3BnxpbDtRj+0CIsKmrZoluy5mwGHPb0dTY3+gLIdNhgN2QjRxOQRW55Is7mkT1UZ2CXXBcEQUCXvIwoWwCDuuaiZ0EmVu6uNHU1A8AwQ7a0M5jbHE+MiZXYJAoCnv56B6Y+9R0+3xAaiu/2SciM4gQ3o86rv+fknc1WDl+jIB/vsTdzWPOc9s5pKPxHIW6adxOmvztdmy9AQLYzG/kZ+eid1xuH6w9ry47reFzENmMVnF12F7yByMUYoxHKtdbP5+/5rQpMRuKr4q8wvPNw2EQbBEFA3/y++GbvNwCgiw2Jhz75fQAAl424DACQ6cjEcR2PgySnZ757qijIdiLbaWu2aKG56w5h+EOLsLtcb8BYu78a32w3L74JAEO75cHrl2PqV0eLfPZ0RxSEMNd/W4XEZoIgCKLV4PNpGwy5zMab81V7VdeGlYOKIFoaPkvcbhNRF4MDuTLo8rvx5JBDzW4Tke1Sf1yu2BNyR7uDYrNVxiRBEEQ6s2a/mk9fHWUo9vZSVbRbuKWFxWaW2Rx0NqdrZnO9J6D9jeCJR8TkiwXGyh9OOw6f3jIFE/tFj0hZds9pWHHv6QBUd29JrfVItF8fr89F1pzNcRx/q3XVUXCqGLqnInS/6PZLWsRFrMiKjGqPeg5fNeqqsOVGIZpx6+mRhV0rfrhrGlbfPz3qeqsOrwIA/G/7/7R514y+Bt9e9y06ZKhC9xdXfqHbJpqzd9PNmzD7/Nko+kMRVty4AteMvka3/JNffYIdf9oBl82FJn9TUgXMQjEa+jbYA3bAOiYlEk6bUyeqf37F53j/4vfx2eWfYVq/aQn1tXd+b3xz7Td4ZcYr2jyH6IBfbv6ij62N0y5ajiBIls+CD4IOVDXp5vP57rOvm6hNnzigEz666QR0yXPBG5Dhk6KL/d3zI8fmpCsiOZsJgiAIInn4m0njjwCjY2fbEfWm/g/vr8PirUebv3MEEQU398No6c5y2GK4q2L5d7+bOgA5QfHAIQrIywgXEpgbsKikDv9dczAFPSYIgojOi98Wo9/M+eg3c77paIx4MT5MNsK+/+rcLSvgsAfeoiCgosGLwzVufL7hcJStWp5GbwA5rnChNJ5oDGcsf6AM2EQBE/rG5gjtVZCliTtr91dHXNcofDNnczxD9o2OSDNe+KYYr3yvFvRz+yVkxBGjoSgKhr80HMNeGgYAuHTYpdoyJpbWe82dzZ1yXDHvh6dvp2x0yY3uHmfYuHzrmybcpBNUx3YbqxWzA9Ss5UiM6DICN4y7AcM7D8cJvU7ABUMu0C3vX9AfgzsNRpYjC3tr9uLWr26NuZ9GBC1GQw8/siAQp9isKAp8kg9DOw3V5o3pNgZXjboKFwy9AHYx8VJlp/c/XcvEBgCHzQG/1HbE5seWPgbHo/GbFpw2sdliNNhoQKOZgh/xevyATuiap15LvxzVDScM6ASXXYQ3IOGO/8SX592WaE8FAklsJgiCIFoNJjZ3znXh9Wsn6JZFyiKkKA0iHWBiMPuhzIbOvnX9JJw4oJPpNlXB2I2cDLv2g9smCjhtaBdtHeb24Yeev/rD7hT3niAIwpynv96pTUcSDiP9HuYfJkcTm1lsQJ2nZQUc1n9REHAw6LD7ZO2hFu1DLHj8EjJMXLlWxfDMSFQAbQ6MjmwmOMXzsMFq3U45Tl0Ew4aD1ZBkBb6AHJezucHXoOUhA0CeKxT9ceO4GwFYx2i0FJl2Vdx/7qzncHzP48OWz7loDh6Y+gD+fsbfcfWoq+Nq++zjzsbTZz6Nc447B/eefC/GdFULId415S4AwJqSNQn3m523xu8P3q0uyQr6dtK7sRfeMdWyTb/sh6RILVK4r605mx/87kEE5ADcfndc2zVntBArbmp8XsY/ZMh22rQHOx2z1e8vl90Gb0DGlsPmvwOnD+2ChXdMxed/PKkZet0yCIKABFJk0hISmwmCIIikWbu/Cmc+80PUXEYjzFX0+1MGYOqg2KtvP7tkJ/759Y7oKxJEM8Ic9icfV6i9Pq5LDk4b0gVXTO5tuk19UHTJcthQyP34d9lDP4KHPLgQt364Xuec3l1O8TEEQbQ8c9fF7/S99s3VmL1sj/b68fnbIq7PXFzGwnHNjcRlNhfmqt/HWQkUkWtuvAEZLhOhtCZKPEmqafA1YMLrE9D3ub7o91w/jHttHI42xD/SzOjI7pijZquu3BNeZNcKXgRrsH+N/Znnot62EAFJQUAyj2fgxeanfnoK/Z/vj8W7F5u2z2czA6rYPKxQdTlfN/Y6OG1OyxiN5mR3VejB88G6g5jWbxpuP+F2OGzhztUBBQPw6OmP4u6T7kbXnK5x7SfbmY07p9yJBVcvwBPTn9Bc1EMKh+CEXidg1eFVWHloZULvgX36RYdr8Ytnf0Cdx4+Xv9+F5btDn//20nrsr2xCTpcPcNj1O3iFXRjaLc+8QQCNPvUeKdPR/NEJkiJhQfEC3Pj5jRj58kgcqku/B1QMWQldJ+tL18e1rasZo4XE4AMHY1wK7+gVBAGdgt8NBVnq+e2yR+7TeWN6YGi3PIzpHbmQaTojCMDhGjfK65PLRk8HSGwmCIIgkuaReVtRXNaA7aXx3Xiz4VJmuYOBKE/T//Xtrrj2RRCp5mC16oQ7e2Q3bV63YAGlk48rxK9P6INTBps/RBFFAW9fPwkPnTtcc5xdPL4nANXdM29jCQKygl8Mj+8HIkEQRLJM7h8aMh5J0FTCJlSW7izHEwu2a6+jxSowwcEvtezQYT6zedZ5IwAAvQua3xkZL96ABJc9/Ge7Me/UjGcuG4Mvbz05Jf0orizGuiPrUO2uxv7a/dhQugGbyzaHrTdtSGTzgPGeb3iwYGBjHIYFXnCqdLwIAKhyvghZUXQ5s4oSirziHyR8vftr7KvZh2UHlpm2X1xVDAD46JKP8OcT/owx3cagya8e78KsQhRkFKDabX1e83mzqWTtkbW619HiMZqDTUc3AQDeWPdGQtszQ/6TC7dj59EGrN1fjacWqgYSdmq89J16j7+17lMExCPwiZHv+Ws8NQCAgozECgHGA4tPeXPDmygqL8K28sgP01oTPuplb/XeuLa1iYIu1iKVsG8AY1wK+1tw55mDAQCPXDACv5nSD+P6qJ+rK+iI7tlBfajQyVAEMMck276tMbCzek3HEhWU7pDYTBAEQSQNu1n4bkd5QtuZic2lKciJJFKLLCuY9UURdh6NfehoWZ0Hd3+yEd5A267cLcsKHp4Xeu/1Hj/eW3kAAzpno39htrbeL0d1B6AOWX7swlF494bJllmZvQqycANXKPCmUwaErcOLPgRBEM3BgcomXPH6Cvz2nZ9xtM6jucgAtcjf+gORxWJW4K+myYffvhP/8HrmaN1b0Yh+M+drhQJf/n4X/r10T6RNk4IJKYIgYHiPPORm2PHGsr3Ycri22fYZD96AhH4z52PlnipTsTmWzOaLx/fCyJ75KenP8oPLAQATe4SE1Md/fDxsvVFR9meM/8hw2JDpsOkKxFnhC8i47cP1WFZcAQBQEAAQEpf31C/H11xdD1lRtFF3fBSJJ6DeY1pFYTT5m5DnysPlIy/HM2c9A6fNqUUnsCJ8r6973TK3efqw5nlQPHv9bN3rM/qf0Sz7iURhljqayxPwQJIl/HH+H7GzcmeUrUKwT3/TIfU6y+BGdrHfA+xhgiKo7k4FkV2eFU0Vur41JxcPu1j3mj2ESEfe2vCWNn3/t/fHta2aHZzc/hVFwb1zN2HWF0X424JtuOOj9eg3c772YMnobGa/Cy8cp5ovJvTtiFnnj0Bm8EERGwVYVq9ev3abgEquqKBZIdW2xokD1Ri+eEcLpyMkNhMEQRBJU9WoOp9e+KY4ru1kbQhr+A+mgzE4doiW5WB1E95evg83z1kbfeUgD36+BR+vOYQf4nwQkW4cqfPgrZ/24fq3fgYALS9uZI985GeGhJlskyJOZg9TzMh2ht8kj23DQwEJgmgb3PXJRqzcU4Ul28qweOtR1Hv0Gcs/7DT//u5ocJW98M0uLNkWf6zCawZB+dYP1wEAnlq4A48vaD7XoKzov5+bgj/ur5m9qtn2GQ/83002uvyVq8cDUP823HPOULPNmo0qdxUA4J6T7sFJvdVM1O0V28PW++3U8Aen0ch02mISV0pq3PhiYwnmrlfjXQJCqW55o+1H3WufpIRiNDhnMxMIraIwmvxNYfm/X175JW6bfBu6ZnfF0Ub1PH9z/ZuWfb3rrCF4+/pJUd9TPJQ2qO/39uNvx/T+03HGgJYXm+ddOQ+AGi2yvWI7Xl7zMi7+z8VRtgohGu6JFC46gY1u8AYkKNyQCSY6W9GSYvPMk2fqXqez2PznRX/WpvfX7odPij16RxRDcYeJUuv248PVB/H28n14bekefLahRLfc6GyONOIVAHKDYjI7TyQZ2HokdA23B2czi/tpIrGZIAiCIIAG7ofp2v1VMW/HbjLM3Dl1wTa754cqc9911pBEu0ikAFbkaU9Fo+7HQSQWFak/yLJMhNS2BLsBZjmRbGj5LdMG6m5uzW50z+FiNiJhtu2EvgW444xBuj4QBEEkQ1m9B/9eukf7HudjB7YeqdMeIAPqkHdjBi5jVdCR3ORT/zawoqc8HbIc6JAVnifLqGgIF5H8koK565ovB/W7HWWYs2IfJEXRFahiLrvmyimNl5KaUEGvxuAxPmdUd+x7cgY+++NJmDKw+YU1Hubs/cXAX2DZDctwy8RbEJDDiz/yD2BjxWUXsWpv9MzmsGH3gl4sbrQvRr3tK+21LyBpMRpMxPEGvFp+7ez1s3W5towPNn+ADHuGbt647uPw/DnP65zZdyy6AwuKF5j29Y+nHYdpQ7qYLksUt9+NK0ZegefOfg5Lrl2CEV1GpLT9WBjddTQA4JU1r2Dj0Y0AgKLyopi3N35L8PUpWOG4igYfmmxLtfleMfyhU4OvAXcsvAOvrXlNi0NpCbG5Y6Z+xNmsH2bFfE/cUvglPx5fGhp1ML3/dADA59s/j7kNQRASel/FR+vx8ZqDCEgyrntzdcR1JVnG6r1VeOjzLSiv92rittWojeO66mNjKhq8uGZ2aB9ymn0OicDiftjf1bYMic0EQRBE0tRzlebfWb4/5u204jwmNxUPnz8CQ7vl4slLRmvz2LAqonWobgwVbyqpjR5zwg+JdZoMAW5LsBgQds4y51+Oy64btmcmGN8ybSAGcFEbVhiH/43ulQ9BEOAIxnD420t5aoIgWpW7/rsJjy/YpnOEMWrdflRzOc0Om2j53ePxq/PZPYCZQOCwiWFDpXlmfhqe+QsA//fxRus3kCTXv/UzHvy8CLKimI6s6hfD93VLMGveVm3a62/973+/5IdDdGhiq8vmgjcQvYhVLH///ZISU2Z3WEExhM5hUVBF7irnS9q8Jp8Eb/DhAeuHsVDa0v1Lda89AQ/qffVwiNai+dzLQgUEL/zowqj9ThVN/iZk2dMnV/wfy/8R/0aGa87LPdx56NyQeF7hDLWtxqXoWX5wOZ5f9Txunn+z5vjuk98n/v4kyc7Kndr+04X3Nr2HB757QHv9u/G/AwB8uOXDmNuwJRijMeNfy3D3J5swf/MRbDwUOZIoICl4auF2vLtiP34sLo/4uxCAZSwdAPTpmIXBXXPj73CawUZguGOIFUp32vYvP4IgCKLV2XR0E7xCqHDH/qomvLdyf0zZe3KEJ9inDO6MhXecglMHd8a+J2dg35MztIIQDGMRwZ1H67E5yo0NT1m9Bx+uPtCqjtF9FY1Ysy/kBl+y9SieX1JsOWy5NaniBAh3hCfu20vr8PZPe/HM4lCGnz9Kwcd0p7RW/UHNboTnrlddd067qBOYzfLiBnXNxbd/mRZ1H/wP8n1PzsAXf1KLOrHrw8pdSBAEEQ/MMcXiqoqPhgoRzd90BEfrQgKiQxRMv3uW7izXnLeNQbHZYyKIOqOIzcbYjd9M6Rfju7Bmy+FabC+tQ63bj6+LrEWg7UfqTZ1wfTq2vphnvIcS0+BXu1/2w2ELCbBOmzOmYfl3nzUEe//2y4jrnDOyW8RilIyA4cGH26bGeu2/Yz/Gdj5Vm19n+wLnj+2CTYdqtfPPJgpo9DXinQ3v6Nq49ONLdYJzZZPqsL7zxDst+3HRsItw5oAzAajHZeaSmXj4+4exp7r5MsYB83iP1mRD6Ya4tzHe8SvBOJszh3fF6F5q3rdH3KJfB17sqNiBDzeHxNJGX6M2/VHRRxjddTRcdlfc/UmGdy98F0D6RWk0+kPH5rIRl+HykZfj1L6nhj1YiYQoJOYUZiNDyuujP4hy+yWsCRaQ9QXk0LVq8hAQiPzgaundp+mictoqIWczic0EQRDEMc6YV8egNOMOAEBBlgMbD9bggc+2YFGEH3gM5mKJNdPWSJPhx9gvnl2K8140ryxuxqNfbsO9czdjW6l5Zl9LMO3p73Hpqyu01799dw2eXbIz6tCz1qC6kRebrcXjG99eg1nztuJ1LoczXYYlJ8quMrUIkMMmos7jx8o96gMCuyjozl/jAxGe88f0iLqf4d3zcOFY/Xr2oJODxGaCIFJBp2xVkNlfqQokFr/rccHYHrDbxLAHu5Ks4No3V2uOZhal1asg/PvPZY8sNvPkZdgxfVjysQPn/msZzn7uR/zlvxtx05y1ljUgfthZrnPTnhEs6haLw7a5ecKQVS2ESXQtD3M2M1x2F7ySN+pQe4dNDCsKaKQg24katz/quWJc7hPVh9pds7tiaMcJ2vxq5+sodasO5sPV6kMRmyDg1q9uxatrXwUA9MhV/9ZWuivxizm/0Lat8dQACBUCtOKGcTdo03//6e+Y9cMs3PjFjRG3SZZ0EZsvH3F52DyWmxwN42iCgKyKjLVuv3Y/VeV4RbdOdoaEyz65DFfNvQrVblWc5AXeJn8T7GLLxbVdPuJyTOg+Qfss0k1s5q9J9kBIVmRUuishybGJmIIgJGXGKSqJ/tvq6a93aNM+iRObTSKZAP25w0crzhjdPdFuph2sYCaJzQRBEAQRZHL/jrrq2+sP1Jj+AKn3+PHV5iPYVdagOXf4CuFRERR4hR1QoMRVqXf57grUe0IxEBsP1mDB5iMAzN1YLU1Vow+7yhqir9iK8Dme2yMI9Ie5nEnGwer0uhGPB0VRsGRbGXuFlbtDuZJ2w5C+AkPBLJ7nrxgbdV9f3noynr1cv54jeNNdx52/BEEQicJ+rx+oasLa/VWo8wRw9fF9dA/L/nHpaDx/xTh4AxLeWbEfBypD3+HGnOUd/8/edQdGUa3fM1uT3fQChCQkIaH33jvSFREVFRUflgf28tPH89kQFAWeCIIIyhMRQRFUFKRJr4FQQi8B0klCerKbrTO/P+7e2Zntm0IS3PNPdmfvzNzszsy993znO18+CcYF+tmTPQqZxKE6LrtEi61nb4m2nXlvFAa1ioRaoE6LDXMewHMHqpouq3L+7OyfGM6//mZaT3SJDUGp1oBrlv+JQqM34Upehe3udiiq1OPQtULsuZzvUYaXM1y1Ob8brrbWYWbNOJFzQrTNVtmslCr57a7giY1GmEoOjgPO5ZQ59PGmEHo2s9DDILmO+9veD6VMiSltX0aLqt8QpVsKAKiSHQEAXjkplTC4VmwtZL3z8Z1oFkBqKujNetyquCX6fxRS5+M5ADzS8RHo3xH39Vy+Y1uY2kBKbgr0Zn2DIJvXT16PdQ+sE22rNHg2h7W9lqkYYUjrSMgkDFhoYZSILfmKjVdwNv8sAOBKESEoM8syRW3+PfDfHve/plg/eT2OP3uc/y1+vvgzVp5c6dD/uz5wvsCqDJ/UdhIA4KH2DwEALt6+6HAfW0iqaaPRKZqo04UZmjc+dpzZkFVsXS94omymY0nLCDVeGJbEb1/6aDfvO9pAIZEw8JdLXWaQNhb4yGYffPDBBx9qBXIpI/JdW30kHf87nG7X7vtjGZj5wym8uO4U317phZ+vVnIEeX5vQCPdw6fu2iLXhuy8cbsSj32djI//JFXTWZbDlJVH670YkJCwHbJgL0Z+tr9e+uEphD6eqw7d9GrfXRfz3TdqoDidVYpDaUSxU1hpwNu/WifxcoH6gk6wnYEqu1wpnCUSxk4BRostbTt/y9EuPvjggw9egY6RPyRnYvJyklnjJ5fy6bsAEKoiRBsNxg5esJf/7KSFvKOo0JmgM5odqlIVMoldUTcA+M+v5/HCulP8+wd7xPA+ncIAdE2s6inH7WyuANgXr5VLGKRklOCeReJ08xlrT2L05wfsVN62ePWnM3h8VTKmr07BzyerX+TQNgjuyFu6LjH/8Hz0/qY3jmUf47fZKps5kC/YkZXC4NaR/Guhz2qfhDC7toA1UHv/ssPo/dFfTvslvMaK5EsAgC9SKJMwYCCDlAsFAGy58R3MqMD644SUlEoYBCmD+P2bqJvgH13/wb9/eOPD/P8JQESsO4NCqhApoKOD6qa2CMdx6PV1LwCAWlH/nuIMwyApLEm0zVN1r+09RAsvyyQMglVyVMhIgccYdUcAsDsPtUE5W3BWtD0q4M6pWxmGgYSR8Or4OQfm4J9b/omd13fesT64wurU1fxrWjQxMSwRAClo6AmkkurZaNBxhApUmgf7OfVgFsJgZnnLPUfFZgEgIoAEuCYJavj0Twx3mznR2KBSSH3KZh988MEHH3ygkDJA11hxyuHZ7FKkF2qQUaTh01hpYbUSrQF6D5XNepMe5/LPgeVYGJkcAIBBcsPpQJxfLi5eV2pRNZ2yLJCvFVRCZ2T5RY/OZMbJjGJcziu/oxWlMwRKMfq9NGQUawxIiFCjY3SQ04WvI2/mJoHKO75Qri1U6Iw4frPYbhuFzGKkeXnOGGya2d/t8S5+ONpOuewO4zuRBZQr6xIffPDBB4/h4HmskElEfpddYp1bCNDnvBkVfOEujd7Ek8oj2lqtMORSCTgOdmOrcJye0jMW8x7oJOoLRW2Myba2GImRVrLOTy5eDsul4nNX6IzQGc1ItlgnafRmkaUUBfUnPXjNaiWQU1LF+2N7C9ufSK28s16kO67vAADsvbkXacWkLkdhVaHIqmB04mgAcOhT/L9pPfnX9Pe8PGcMfnimj8PzhQmyglypKYV2UjopKSJZZSTBE764GAL4NhxjFR+U6YvAgIGUkSL39VxEqiMxd/hc9Igi9huHMokNG1U2uyoQKMSNl2+g6K0iqOQqtAlv436HakCoGm4IymYA6BXdC+mvpGPDgxsAgLe3cIdKPZm7D2pFSFAq+JBJJWgS6Icn+pNsg5uvnUHZrDKsnria31cpVSKjLAOXbl/C6VuneSIVALo2825uVRvo0qwLMl61qrALNAUuWt8ZcBwn8lKn39HYpLGID4nHqVunPHquEmWz989f4S4tI9X4640hzhsLkFWsxa6L+fCTS6CUOX7ehakVuDB7NF4cTgIQlz4cg++m9/a6jw0NGaUZfJALIEUCvcnebajwkc0++OCDDz7UCm5WbUHrpgGibVfyKjB04T4MWbAPg+bvxa2yKn5SWSWoEO5O2fzWrrfQ+avOWHduHUrlpBgHB71TsrnEpsiMMJWV4ziM/pwoljpalKhLdl/D5OVHMebzg9hx4c4pcDe6UT3VZ+FCRyjRGhCmViA2VOW04N9HWy/ZbYuPUDfaqspPrDqOT7ZdFm0TKvipstlPLvUoVVilkHntUS6TSqCQSaA1NvyAhA8++NDwoXMwdsqlEgxMshI3rgotGUwsOHDI9n8UxcpFAIi/JCX7egnUq1TFaKt6FlpbNA32E5G8wuCk2UuywxGJYjteCfti64UvFzzHjWYOnT7YiRH/3c8X6HtrUyq6zdkFvcn6HW47dwu9PvpLVOwXAL7afx0d3t/hVf8pbNPI+ydGOGlZN9ifQTKt3t7zNlp90Qo3Sm4gOTsZerPVNoKqeJckL7HbXyaV8IQ5/W395FI76ymKqGA/j/ol/O0YjhDUiaFEsUkJawbWa5eD9TqbvnU8tl7bin6x/RAVSIK4EkbCW2lQeKNsBoBQ/1CE+YchKSzJo4KJ1YHQD9mdl/SdRFxIHGKCYgAAK0+t9GgfGoBpGkR+c7ouoPMphjEgWBkMmVSKIGUQ4kPiAQA9onqgT0wfbEvbhvZftseVoivo2KQjf1x/efUtd2qCFsEt+O+A+n3XJ4QWGoDVm5xhGLSPbI/00nTsurHL7XGYatpoCOf7vePD34AZkgAAIABJREFU7LJHnGH98SykZpchyM/1fadWyngls79CKho7GiMKNAWIXxyPN3ZaC5L6lM0++OCDDz74IICeLcGgVpH48bm+/LYCm0rERZUGflKpNZg99mzO05Big5duW4lMFlWodOJhW1RpneybzCz/Xmcy8+l6ANDMMtE9nWmdHJZVebZQ0BnNNVZcuYtaG9yk695p5JbqEKpSQCGTOO3b98fEPnvbXhkEtUKKcheemQ0ZZ7LItdEkUIlFU7rYfX6nUvdUCim0+sY/8fTBBx/qH46Cf0qZBG+Msqoy/Syk6/43h/LqX956ysyCA8nMqZQQUlJjMPGk7qO9WvDHoaSyrZWG8L1twFmopvOW7HBU3M92vBJaVNgWllII0rdNFg+PnNIqPouFBqRLtdYxjdosncspszt3dacJNO08IkCJHa8OxswhidU7UDVhq+pNL01HgCJApNxtHtgczQKaOSVl6f/uiVVaUpNAj/plEviqyDhCEi8YtQAA0K+l1X97/sj5pA8WspkDh6yKG5jcbjJ+eOAHl+fwVtlMoZQqUVxVXCe+vfkaqxDiwfYP1vrxa4I+MUStXqazv/4dQWOZywQoCQnJK5st91ipvhR+MmvwITooGseePoYtj23B2klrsXjMYv6z7yd9z7+WMPVHbZ2bSby6Han87zRoYOLnh37G8WeO80Q4AMwdNhcAcL34utvjSJjqiV6qjGb0TgjDqmk9MWtsW7ft7+3SHC0jrNkm3mb/NXbQAMWGCxv4bf4KGbSNVKQjhI9s9sEHH3zwoUaQMmSyqDWRhWfnGKtvrdFku8Az84tRE8vx1hHuyGZaOfnjQx/z27Sy/Zj0w0v8+9e2v4YM/wkAIFInT/v2OF5aTyqSZxRpRcUoYsPsUxEdeUvaIr1Qg7bvbscn2y+7besKh9IK0belY/9CANh7uf7T8Sh2XczHzUINQlRyyKUSu9+Wwla91i4qCFIJg8t5FSiwsTdpTOjeIhStPFwM1wXUClmtqRy+OXgD8bO23hUpej744IP3cJZpIsy6oArUuHA1Xh7RCoBVIWwwsdBLxOOfRm/mSVdhlkegRaVmm44tDNbKbfw5qeIxIkDpdVDXUdaNcJveZEaeYCxqZZORRQkvAGj/nlWVXGnj+ywkm9dZPIG/OehdLQNXkFl+C5bj0KZZoEeep7UJW1/gBUcWoMpUhVZhrUTbu0d1d+vV60nWj6egSnQ9kwa99CwGxw3mfZglEoYntnmlqXw12Y/Jhok1oWuzrmgR3EJ0zNbhrUXvvVU2U0glUhzMPIgJ6yZ4tZ8neO6P5/jX7goX3mlIGAl6Ne+FzVc2Q2PQuG1PPdRpQVGDwKeX4zisSV0DpUwp2qdPTB80C2iG2OBYTG43md8uJFLrEyF+IQjzD8Pi5MXYeHFjvfaF3o9xwXHoFd1L9Fm7yHYAiMe0O0gl3ttotPrPn0grqERsqAoj2jVFiMr9tdotNgTtmlu91G0tGe92UM/5fE0+9CYi0lL5CgT64IMPPvjwdwfHcTBzZDCs1JMJpkohw+eWqLRaKU6d0hrMomJ8xRa7C3eqF42RHPvz0Z/jo+EfWY8nPci//jz5c9InGKEUeDBmFGnRJSYYTQLJxJV6R/dJCMPoDk3tzuWowJEt6EL111M5bts6Az1P82B/DEiyqnFGtmuC/xvVWnSehoCr+RUAgBlDEl0qm2kxu6WPdcOBN4cBAHpbUqptle6NCWEBClFq95yJHbBxRr87dn5/hRRVtWSjQYs7FlY23t/DBx98qD4cFblLjCSk67f/6IXFj4iVZbTAG33uG80szIzYMkJrMEEulUAhJd7Pa6b3xhv3tOYLotqOrcK3Cps06IUPdcbHkzpheNtIr5XNjgr+CsnmSkuQe1q/OKz+Ry+0bRYkaiv3kBgV2mj4WfxFc2yKE9cElPh3V5CwrmAwG9A2oi2+u/87SBgJ9CY9NAaNnV+wSq5ySzbb+mI7w7ZXBrltU27JaDNKsgAAr/R5RfT5r88PwLpn+6CoqggAUCVNAQCYGJIhR/2ZhZg3Yh4SQxMRrCRiCWqF4a2yeVAL0v9tadu82s8TVBjIHCz5meRaP3ZtYHjCcABAToX7ebHGYEM2C2w0qDd1vxjn86vooGj89OBP2DuNFC3d/eRu7H5yd/U7X0tYO2ktAPAe5/UFumZy5O3tJ/ND24i2HmXlVcdGg2aW+Cvs7/ktLw3EwbeG4cup3UXb/RVShKrIvdYzLtRu7Xi3g5LNAFCuJ4XjfTYaPvjggw8+1CuyirUYPH8vDly9XS/nX3VqFUI/DeXfZxh+xrWiawCA+7tFI8hPxlcipnjq2+P45bR1IkqL7LhSNutMOmxP246BLQbilb6v4O1Bb/OfmSR5CPs0DGGfWtXBmf6TsPLKBLz+8xF8/OclZJdUoV1UED6eRIoP/WP1CQDAlF6xYBhGVCgIsPdvdIRHVpLq7N74EJdqDRj46R68voFUbacWIm2aBWJC5+Z8uwd7xOCZQS0BkIJ8Pef+hf319BsL8eXeNMilDJKaBEAhlYh8iynMLIdynREvj2iFCZ2bo0U4mei2akoUwc58nhsDmgb6iVR/HaOD0TPeuSq9tqFWSPnU05qCqszK7pC1yUNfHeGzC6qLmWtPotdHf+H67Ur3jX3wwQeXcBRUpZ65w9o0wcSu0aLPqCcmzWgxmFiYGbIoptlN7/x2Hrsu5vMey4NbR+KlEa3452ZuqTh4KlQsK2yKQSU1CcRjfVrAXy512NffTucgftZWrLWxbQKA9CJ7ZaXRJPBothyvdbNADG3TxK6trcraGSipojWY6qQmAf3ePAmA1zZO3zoNrVGLye0m48kuT+K+Nvdhb/pelOhK7Hxx1XI1Lt2+BGY2A2Y2g/Hrxtsdz132GkW7qCA8MzABahd+4bstGV8syPU3OG6w6PP2zYPQPzFCVMiwWL4C5bI/ANirmAFAKVPi4Q4Po0xfhgH/G2C10fBS2eyKIK0Jhn03DOml6Xi1z6voHd0wi6ENjR8KANifvt9tW3/L9RCmJiKQFQeI9YRMIkFKLgkO3NPyHpfHeLjDw/w5hycM58nu+sSYpDFgwLgNvtQ1Vp4k3tnOCkne1/o+5FbkYv7h+S6PQ6e87rJLckurMOCTPegyeye/zd/BPd8xOhixYSr0jAsVbfeXS/kMmMGtI12e626EsDAgDSr5CgT64IMPPvhQr7icV4HMYi2+O5JeL+c/mHkQZXqxP9uF2xf41wqZlFdB0aItdM3UrQVJkSrmyWbnwxGt7JwUlsRvW3P/Gv51fEg8Hu/8ODo37cxvM0lu4cdTJ7HSMoFVyCSQ2Swg48IJyUyVuLyKiPWcENUbPW97o1CD7JIq/GJRQ9PFqUohxQSBZ6TOyEIpI4V1zmaXobBSjwU7ambXUVOQytYsr3xTyiQOieOyKiM4DghTiRdocktasiMvzcYCM8eJyGZPC57UFmpz4kkzCWwLadYFdEYzTqSX4I/U3BodZ9v5PNyu0ON6gY9s9sGHmsIRf+mqICAlmylRW64z8WSfVEL2yygiBIstORoTSsjJnFIxASNs5ozglThJ435r01kAhOC2hS2pDQB6wXhFxy65xPG8w1Zl7QxUcVxYUTfPUVog0BNrr9rGmTwSFKdk3lv93+I/iw4UByJe6v0SZvacyb//89qfdsdzRDw5g1LuOJhNQb8XliHXE1Uj2+Lpbk/zrytkf4BlSBCiZWhLh+2f6voU1HI1jmQdQU55jstjO8PopNEAIPIbrik4jsO+9H0AgOd6POe6cT2CkuDCQobO8O697fHBve0xuLW46GViZADyKokCvU1EG0e7NmgwDOOR0r+uQc9PCyva4unu5N7Ym77X5XFoNp+7gNeVvArklFZhUCtBgVkX97yfYKx5fmgihrSO5J+79fG8q28Ilc0VekI2+5TNFjAMI2UY5jTDMFss7xMYhklmGCaNYZifGIZpWKZCPvjggw93CejirL6KyH2X+h3/OsBElCwZpRkYsWYEeq7siYwqa4Q75T8jMaVnLP/+6YEJAICfT2YDAJQWVdORrCPo+01f9F/VH+cLzsNgNmD0WjJ5Hxo3lN//iS5PYHLSvwAAJcXtkZf5KBYNtxZWAACNdB//2sxydtWKm4eQxcCApAi+DeDdRMdgZvHnuVsetT1+U5xynGFRXyktEf0n+sYBIKm5DMNALpHwiub6rrRcZTTDaOZwf7dovj+OUpXPW4ojharFQz+9Vhubslk4wZYwYj9TT9OCawtqhYxPPa0p6P32xKrjmLvlYq0c0xn2XamZKt/Mcug59y/+fWMOWPjgQ31j+/k8PPDlYTv/YQOTjkd/uwfzDs5zuB8N1o5fchBH0gpRrDGgQv4b2desR5XkpNNzUpsKYeFeQOzh7MzTV8IwfJG5YzeK8PCKo7hZqLEbS3JLqxA/aytGfrbfodrZaGKx62I+7lt6CF/tvy76n5z9r+5g4ucMdTOuUY9m2R32agasqdzdmnUDAPSL7YdRiaMAwM4DtkfzHlg6bqloG1VCtosiv/2nR9/Dr5d+9ejcSpkUJpZzaB9yOrMEv6fmomWEGhx0ACd3qj6WS+UiFbBBcgUTkh5yah/QOrw1vhj7BQBg3iFyH0SqvVNZquQqPNv9WehMOp40rSkoETVn2Bzeb7chIsw/DGq5Gre17sf8ID85nhqQwAcOKBIi1DxRauur3ViglCmx4uSKO37e1LxU9F/VH8/98RyOZh/FIx0fcXmtj00ai+1p27EvfR+GrB6COfvnYO6BuaJ2Ut433vW5qXDozdFt8GAP4qGtdEE2UyJaKmHw1pi2CFUrrOf6m5PNM7bOAEAELVqfZzMA4BUAlwTvPwWwiOO4JAAlAJ52uJcPPvjggw+1AlcKkDuBcOkAqM0DAQDHc49jz809OHnrJLSS43wbiYQRqabCbMhIOsnYnrYdyTnJOJp9FIczDyO9NB2XC4mql/q4UYxNvBcq0yDoy/vj+M1i7LlggMo0BFKWLA5MjHXCm1mstVuwWdP4xH0xe0lmLdx5xaN2dDIWaPEiyy8nfrm0ENL/jWqDJ/rG8enLRsECNs5BIcM7Cdr3MEuhD4VMApaz95KknsxUyUZBPTAbG9ks7O/TA8ULo+gQf0e71BlqU9lM/csB4JtDtVfQyhEu55XXaP9bZVUib2mDufErPXzwob6wISULpzJL7bZ3TMjBqbxkfHbsM4f7UdVZYaUBe68UoFxXCQ5m3qpAJznDt137dB/RvjR7qFxns3AWDLXO6jZIGCsp/XNKNo7fLMaJm8WwFTtTO7G0gkqkObDaMZpZ7LlcgLPZZfjtNMmykDkJ4gqDu11igvkx29Exgbq3ufh5Rv86Pb4j0FTuQKW1KO68EfMwrcs0dG3W1a49wzB4odcL/PsqE/GuXvF4Dzw3uCVWnl6EBzY84NG56bXgSEjx6NfEwkxnNKNrnB9C/FwX7X2p90sIVlpT9vvF2vddiJEtR2Jyu8no2bwn3uj3hlMbAlfoENkBAHg7iJpCbybjn1KqdNOy/hGhivBI2UwhsSFD/RVSnmyuznffEBCkDOKLvN1JHMw8iKPZR/H1qa8BACFK10X2aFHNz499jgMZB/Devvfw7t53RW3oz+OuSCDNkgtVK/i1VoAL32W5VILXRrbG5hcG8Nus2aV/b7L5WDZ5xpE6LY1/vlsjsplhmBgA4wF8Y3nPABgOgJbg/A7A/TU5hw8+/B1RqjVgwCd7sNfiS+aDFevOrcObO9/0ujp5Y4GJNeHFP19E32/6YsrGKVh6fKnTtnTwd6QwrWsIv//u6o8woQ3xzBNPMskgGd7kKPal7+M9+/okhDm0IDCxJsw5MAcShgxNX5/6GiPWjLAejRMPumPadkek8V9QcEQRvDY5E5HGN/FA1GYo2ERwjHWyZ2Y5u4Ul7U/7KHFxoMW7r3l1fXmy0LyQW8ZbesAyeaPEYcsIi52HSo4593fk+9VBUJnZVXpzXeNEejEGfkpS7ahiWSFzbItByeeoYDERS4mKxqRK5TgOL1t8ht8Z3w6BfnJIBYo3Z0RFXUGlkOJGoQa/ns6u8bFsA1R1FQQ4cPU2Pv+L+LhXVwl+JK1I9D4lvQRvbUy184P3wQcf3OPQNcdE0PD2ZLzRmRwXpRUSsL+ezsUfFwmRtmzcMkSqIsFaxtuXR7TCwFbi1Hi1koxf3xy8IdouJDCcZe9IGAZagxnfHLwBlWUcLHZg/3Mmy0qg367Q49HesYgIsAaSjWaWf85RVbfciWKYjldPD0zA5hcH4tzs0aLMLApa38ETcoQWtfMGJjOLzjHBaN88yH3jGoLjOLy9+21M2TgFz299HsVVxVBKlVBIrd9h96juWH3/aqck4NJxS/HluC8BAOqP1Vh0dBFahKvw9jjv1LiUbKY2ZWkFFbwaXWfZlm38BaXmcwh2QzY/3vlxlM6yZpU90N414R0bHIuND2/Else2YOGohV71m+LeNvcC8MxOwhNQ4lIpa/hkc6Q6stpk8wvDEmE0G/HWX8SypbGSzU92fhJG1njH16m21h0jWo5w0pKAemJvvrJZtF3Yb/r7aPTOFbYcx2HxX9fAMERMQ0ljqZuMjFdGtkLHaKtNDSWpOTSedUJtQUg2A8Cmi5ugkkthNHONTqRji5qulD4H8BYA+i2EAyjlOI5+Y9kAoh3tyDDMcwzDpDAMk3L7dv0XPvLBh4aET7dfQU5pFV/IzAcrpv4yFQuPLkRxVbH7xo0Q14uvY9mJZUjOScaGCxvwyaFPnLalC536KB5DKx0HK4NhNHPwl/tBLpHjtsb6PPdXkn6dqvgIw74bxiuJVQqpw+IvN0rIQrRVWCsAwMlbJ5FdbiXWbAfjpgJ1ZqTg9b/HtQXDKcFBj4gAsv2FYUkiZfM97ZvyCxrb9F0Ty+F2heeqBNs0QEf4/YzVr5ZOHGjE2lnhnAu5VkVoZS0VhqsOaEFDAOhkmRhSYsA20GG0XIu2ach8calGNGlKL9Ji58V8ANZUYE9+67oCJTRe+ym1xseyteMo1dZNocDFu6/xr6sbaDiTLVZh/pCciQ0p2UhJvzvHAB98qEsIM2aGtyXF8T6e1In3idQatWA5++e00FO5sFIPoyQLABAXHAeVXAUOZMzcecHeOoAG5m6ViYlslgNiw/zRJSYYbZo5Jg2plcTcrZd48qJSZ0KfBHFx1h9PZIneByhlvF0QABjMnN145SxgSOcEQrLE6MAqg9pneDIHO5lR4raN/fG5O2ahUaYvw7xD87Dl6hYsT1mOE7kneOWjNxDu8/rO16vVF5p+T4Oij6xMxifbLvNBAg4ssrgvcTb/LAIUAR4dc/7I+RgaP9Sph21tIlJFsutqi2w2mElwRUj8N1SE+oWiROf5tc4IbkF/uRSXCy9DZ9Ih3D8c/rI7mz1WW6AkubPAXV1BYxAXRo1QRThpSdA/1nHGhDCLlAbebhTaF12lyC/Xo0JvQvNgfzAMg0d7t0D3FiEY4mWhv9EdmqFHXCge6Bbj1X53A2zXt18c/4IXGTV23+Zqk80Mw0wAUMBxnHOTLhfgOG4lx3E9OY7rGRn596s66YMPrkDTARsS0goq8O3hm7yHbH3j/X3v48sTX9Z3N2odtpFpOsl0BHdpTXUJujCdN2IeDCYWSrkUaoUap/NO821ahCnBwTpI+ivoAk7iUKlL//d5Ixx7RppZ8YArXCge+7c1gt+tRSgSwkOhk6YiT5ONB3vEYEBSBE+AJkaq8fWTPXkvM7qg6RVvTbVMzRYXPnQFZ5OwMq0Ri/+6hkdWHsWKAzfQLMgPLw5Lgs7IYsqKozh2gyg2namWhT/vH6m5Dv0L7wSyikk67LB2/tiZTnyx6WJcb2NpYHJSeEnWCD2bhWv8/onhANwrNeoSwu/0RA2JVq3ejNEdmmLpY8SPc5eFVK9t6E3W68PMckjNKsX87Ze9Il5KNAYkNQlA6nujRNv/jqmWPvhQExjNrGhcCQxKw9NjT6GY+Z336GU5FmU66/hnZs2Yf3g+ztzeDz1zDSWy71Ah3Q6NlGS79IruRchmhhArtmnxrsCBw7iOUdj84kDEhDpWMQofuZRsNLIsyqpIgIwGlG0hk0owoYu18K5Q2Wxt47ivNAAsJJFNDoJlNwu1uJpfgX1X3GchpmaJg2abz+SgqFKPCp0RG1Ky7NqXVRlx8Fohr+StbRRqC/HRgY+QU56DtOI0vL6DEMP3tyUJyYcyD4ksNDyFo328VXhSIcDmM6RIH7VRKrFks7CwkmGe9vHNAW9i77S9tVq4zxkCFAFQSBW1QjbnVuTy/tGNwUYjUBnIrw88gfB54Se3WmismbTGqd9wQwclm1edXgUASCtOwzt73sHbu9/G27vfxtqza+vkvFqjFv4yf96n3N310i6yHY5MP0JeR7TD6omrAQDXiq0igU4xRGCic2HnsGQPaf/uBJLB0DE6GL88PwCxXtr/xUeosWlmf6eBx7sF3535Dim5Kdieth3zD8+HmTXzZHPyM8mY1HYS9mfs59eGtWWfV1+oSSn1AQDuYxhmHAA/AEEAFgMIYRhGZlE3xwDIqXk3ffDh74X69uB1hJGfHeBfp38yvh57QrDsxDIAwOR2k9E0oGk996b2ICSbAxWBvFebI9Qnb0f9/Ig3GQu5VIKogCiU6siCSi1XQynnAFk6v49CShYcUgkpdkbRNZb4igl92vrH9seRrCOic97X5j67fjzVPx6p2aV2JGDL4M64XHoYldK/oJT1AAA0D/ZHuFqBF4Ylidq2aUomNi8Ob4Xn156ExmDGgau3cU/7ml1X+64WYNFfV/n3UgnDK2STBcUC/Zx4Vdpi3fFMPNkvvkZ9qgmumz7D6t+2oluzblBKyW9mb6PhWNncGG00hIsguuipT7JZ+J0+9NXRGj2HNQYT1EoZ2lom9b+ezsZjfWq/GI/tJPmVH08jvUiL87nlWDO9t5O9xCjWGBCmUsBPIb5PGlPgwgcfGgK+O5Iuer/k3GP86xEJ1oBtobYQof4k+Hq+4Dz+9RcpxquS94dWah2XVUw8QvxCoJKrEKrmAAPw5pg2LvvAshyvVmY5uCWUWje1Eg80ldtk5ng/X2dB9xKNASEqgY2GibVTNiucKJtpFpbwGfNEvzj8nporaldQocOoRda5sZ9c4pQcLhBkSxVW6vHKj2fQrUUImgf7Y+u5W2gfFSRKKf/xeCYA4OKtmnneO8P6c+vxzt53UGWqQlpxGn668BMAICEkgW/TJ7qPs92dom1EWwQrg1GmJwGLIm2Rx+pjCpoJNW/bZTw3uCW/nfrCsoz1O/H22HcCDMN47V3sDOPXjceZPJJd1jaibY2PV9cIUgbxgStPIJxSyaWSRu/XDAAdm3QEALy07SW80OsFLDu+DJ8nfw65RA4Ta4JMIsPjnR+v9fNWGCoQoAjAe4Pfw73r7/WomGSr8FZoqm6KWQNnoamarHnO5Z9D96juAKzPQmeEZ0GFDuuSybNK+Kz2wTHMrBlPbX4KIX4hKNOVgQOHEQkjeI97P5kfXwfBDHIfNfYigdVWNnMc92+O42I4josH8AiAPRzHTQWwF8CDlmbTAGx2cggffPDBCai/3aBWrlNg6gv15Zd8KPOQ3bYvjn9hp3htzKATrYP/OIiZPWeiXF+Oc/nnHLalKZx3yt8qvVCDzCLSv9wKsugKVAbCYDJDKZPg54d+5tsGKALgrwC+fbojv03HEoJValMs8DdLgYhPD38KAFAr1Dg8/TB+m/Ib34Z7n0ObCPtF7Af3dcCvzw+w2/54+1lgODWqpCm8SilUrcDJd+/BA93FKVqhagXSPxmPIa0jceHDMYgPV/GqKU/hqHqyrRLgxeFJGN85SrRNIZV47P17IaduFp3uoJBKMGNIIiRSoia6rb0NuYysEGwX7xnFROVt679ZmzYaKenFLv3jnMHMcjic5nzxV6EzYvOZHP7YjvpKF0aObGDqGrbp1DVRI2eXVEGtkCGpSSDGdGjm9nq/XaHH76m5Xv9+JVojHu/bAuueJaRFuuX5UeGFf2mJ1oBQtRxKmZT3bAUI4XToWiHe33weF3Pr597wwYfGhNMCdS2LKtFnBzMP8q+XJC/hs6qEAW/hPkpzZ3RVrIKEkUAlV6FVMzKODmvTxGUfzudaVdMcx8GdeHFi12jMvo8UXNt2nlh0mAQKbao+TmoiJh2rjGbRsY1m1q7gnLOsIj8HZHOv+DCkfzIeE7s257dV2BQ8DPSTO/0/Dly9zc+d6bh5OrMUhyxjku20utTLOYi3+PXyrwAI+XFba81YbBlKyN1Qv1Csm7zO6+O2Dm+N0lml+Gr8VwCAM3lnRJ6wVcYqZ7vyEAZ1hery65bCj2YB2Sxhqk1l1Clqg2zOLs/miWYA6Bfbr6bdqnMEKgJ5MYonEAb19SYzvwZSy9W13rc7hWEJw/DpSLKe+SrlKyTnJCMxNBGGdw34YOgHMLLGaq1bjWYj1p9bjx1pOxx+fiz7GMJV4RjfejzY91mE+LkuEAiQ6zTv//LwZJcn0T6yPTkPa3328OpaJ8pmmvm4fGp3tIxseIGfhgZqrVKqK+XX7j+c+wF/XP0DALHgmdhmIgDAwJLnXFEjr09SF0/ofwF4nWGYNBAP51V1cA4ffLirYbRMRB2l7TUEVFaD6KkNjPthnN22jw5+hMNZh+uhN3UDOkkLUATwxUA6f9XZYVuq6LlT3P/QhfsweAFJnT19i9hlRKoiYTRzUMgkIiVCVGAUjKxRpHAoMBCv2e4tQvk0yaf6xwMgkfTfr/wOAIgOJFb/LYKJ0pJ6OLtDUpMAhFkK2EWH+APgYJBcgRNLZKcIVim8XuittlGMAfZEbJCDhagnRdNoYaLqFBiqKUyWBbq/XMoriDQGDRRS8qXa/o9rjxGFgy0xKq8lG42iSj0e/Ooo3tjgvW/xl3vTMPWbZKcFstYey8QrP57BplO1SnLpAAAgAElEQVTEJ9yRTQMlIZ4bnOj1+WsKWwL/2TUpuFXmfuFui+wSspgzWx4cISq5W8/mOVsu4uX1p3HkepHLdkKYWQ4lWqJK9re5CSt1no8hxRojf18Ln3VaoxmPr0rGd0cz8MjKox4fzwcf/q4QPq810n3izwSWXUtPLMWu67vstlNfZgCQc9H881wlV9lZgDnDh39ctB6PEysbncG2roKRtfovUzJSaNkDABq9GYNbWW0aDWbOLgBs+1yioOOXwWQ/BozvRALGfnKJXZFSveD4wuKEAAnwbTpFkn2FxA0N9JnvoIijVFeKvelkLmfrFdqpSSeH272FXErmOyO/H4kpG6fw2z0hYIUEpJnjEOxPjnX5Fpkfs7AGLHZe31mjftYVIlQRIhK/OohdZC1K2bVZ15p26Y4gUEFsNDwVJQkDQm2bBd0VymYAaBNOxDHP//k8jmYf5cUy1HfblUWiM+xN34vHfnkMY34Y41A9fqPkRo3EV476Rue8zmw0Tlks0ZoF1709zd0ARz7ei44twprUNQjzD0OEKsLqtS0hz7uzXtg6NkTUCtnMcdw+juMmWF7f4DiuN8dxSRzHPcRxnOdVlnzwwQdkl+Uhu4ws6O/k5NMbOItw1hVyK3KRUZqBCkOFqLjH/qf2AwDyKu0L0jRGlOvL8UXycgBkouqsGEh6aToySjOQXnYVBiYDAFEIpeSmYO/Nvdh7cy/+vPYntlzdUivfTUG5zi6Fii5E2kd0RJXRDJmEgb/cWswjUhWJIm0RTyADQFr5QZx+dySeHpiAcn051j4finfGkwkYTSECgIRQksbZLaobCt8sxNmZZz3q585XByPlPyMBAINbR2JE9D8BAFqTdwO1UirB9YJKp5/TSfRLw5MspDaw6tBNHLleiNOZJbzK+Uo+mSj0iCPpyHRSPaKtVfnliSfdG6NaI1Ap4xdcdxIavRkmpgAmlPCpXSbWxC/+heTxtktn+Ei9rd2EzElBQW9BI/x73Xhk6oxmJN8owk2Bnzb9PYo0jqcllXqy6L9xm+xTUE7aLXusO9/GTy7F9Y/H4eURSfYHqGOolfbOZ576ebIshwu5ZSis1CM1i9wPI9uR69BfIUVBhd5pkasSjYFPH6dey+dzytyqk89ml4LjSOaArYLQU2W60cyisFKPUEs6/MyhVpK/XBAQKveCvP67o0RjQFktFIS8WaiplwCYD57DaGaRU2odW4P95XzgxszY+77P6DEDp/9JAsk0iG80W39jvfQiQpje+N/oEwgzzuADciq5CsdzjrtUrd74eBx6J4QhJaOETwtmOc4jj2fbQJvJzPJ2czzZbGRFqmMTy6JfYjiufzwO0SH+MJpZlNhc987IZli65Khrozo0Q9pHY9ErPswucDmyHUlDH9OhGY6/PRI3Ph6H87NH85//nJKFm4Ua3ntYiKt5FbhVVoWCipoVFUsvTXdLOF26fYl//evlX7EvfR8GtRiE0n+V8spmM1ezeb5cIp6vDIsfBgD489qfbvcVTh/MLIdwC3EvlTAIVTFo0qTmRXLrGhGqCJzIOVErmaAKqQLHnj5WC72qewQqA2HmzNh5fScKNO69zIX3/+DWkfyapbGTzRPbTkT+/+Uj67UsZL2WhV+nkEwC6qPsyiLRGYQq94MZB0UBoZslN1FhqMAD7R6odp+pwOly4WX+urUKRRxfxxWWuVwngQVQYwPHcbhefB0A+R4dFcitLdiSzdO7TuevkYxXMyCXyhGuIjViWBkJTmrrSeBXW2iYuSc++PA3RuznUcjxew6A49T8hgCd4c56ZUZ/Fo34xfEAgAmtJgAg0fPW4a0B1F7F5/rGkNVDsC/jLwAQRzdt0HJxS8Qvjsfr+0fglt8LMJgrkJqfil5f98LwNcMxfM1wjF83HveuvxePbHykxv3q/fFudHh/u2gbVR/8dIKk8p9IL+aVr892fxZh/mG4VHgJ3575lt/n+7Pf42jubjAMg2m/TcOgbwdg+3VyXGdFW8JV4R4XdJFIGN4LEgCaBRJV0/WyEx7tT3E8vRg5pVVOiRR6W8okEn7hnlNahce+TsakL4/g2M0iGM0sr/Kl3s/x4SQt8JLAg9GVfQFVUKmVMgT5y+vF7/i9388jR/kMFp5+AlKJReFg0lkLBFoW/D+m7sS4Dd2gkZLr15ZEp96YNS3qdtSirHXna7/6SDqmrDyG8UsO8s9RmiliS1xQ6I00tZkQqo+vSgZgrz6XSph6KVxDgxYDksL5bc4IYlvsvJiP8UsOoefcv/DCulMAgOgQspijquZFu6463PetTdZgz5Ld13AlrwITvjiE//vZ+YL/Ym45Jn1JvF0D/eQIU4kDZxUeTp5/O00m2/Q+iwu3LkBtU9h98Azd5uxC1zk1UwOaWQ7DFu7DCz+cqqVe+VAXmLvlIgZ8sgelFq9bo5lFkB8JWgl9bylS81N5Rd68Q/PAcZwopRoA4kObo32TBDCQ8gVhm6hJ4IpaYTmCRMLw9kMvriOENsvxvK5L2D6DTWaOD3Ralc0sH5SibQDyvJZLGRjNLEq1BpEVjzMbDXeQSSVoHuxvZ8sRSL9bjuPnIwGCIGHyzWIMW7gPG09m2/1vb206i/FLDmHiUnGm3uDWkfAUGaUZSFicgPf2vuey3azds/jX10uug+VYtI9sj2C/YL7gXk1IK8CqbKZ4sD1x2JyxdYZbEtLWRoPytSwH5LN/4ELZFl6MMSZpTI36WVcIkAfAyBpxLLvmJHH7yPY8EdjQERVA5q1jfhiDqb9MddteaplL9WtJ5jUvb38ZQOMnmwHyXIwJikFMUAx/vdLfUW/ynmwWrqcmrJ+AL098yb8f+O1AAEBiaPWz7igR/sXxL/iilAo3FnjEG1/usR1gQ8Q3p75B0hdJ+Pb0t2i5pCUWHllYZ+eyJZubBzbnrxG6ho4JIlaPq8+sQoBSZhckbWxovFeGD38rsByLzLJMFFcV12nEqb5RaSBqSpYpAwdDjYmZusKdUjZzHGdHJC8aswgpz6bgwvMX+AdzY1Q2601mu7RPGrUOMk6Gn8wPT3d7GqMSRwEgapUibRFMrMnOo9nAVeC2hqTrfTH2C+ybto//rKjK87R3V2A5a7ECgBD8MokMR64TdVRuqQ4BigBkvZaFZeOW4asJX4n6sW3qNgBAVlkWCjQFSM4hRF6RlvSPqqd+evCnWukvALQKIYpUrcm5StkVHKmPAKt9iVRirZoOAGM7NgMA5JXpoNVbf9tnB7XEwbeGoX1zUhzQ0/tnwUOdseeNIVArZZBJGd6juzZhMLFObXEMJhbbzuUBDIs87VX+XtQatbzaoVynQ0ZpBrZe2Q0ASIzNwJ43htgdi7a/XeHdBLtYYxCR/rSvUcF+LoNxVHWrNZh5Ypp+f7YWHwCx58iy2EtwEFuWSOqxKKAQg1tH4uBbw/DhRKsPurPiWLbIKhanuIeq5HzFb6oQ/uVUtoi8rtSbwLIcijUGBCplGNaGkB43LL6ZOy7kO1TIluuMOJtt9YbtnxiOJkF+eP/e9vy2Cp3JI8UXLar1SG9iqTOhc3Ose8bi/yxQrTcL8qVwuoPWYOJVrjUV29EMhYNOLGl8qH8YTCy2nL0FACisJGNZscYAhUwCDiYYmBtoqorDkelHsGTMEgBkvuAv98cz3Z4BAGiMGjt7jJ3T/8eTtDTFesE9CwAA14qvuewT9VXec7mAv/89CdyF+IuDVcVag539hN5SO2L+g8R2TEiOyKQSMhYYWZ4QBoiFUHXxzoR2dvYeKgux7OfGu4sG21pGqkVFCos1BtwqI2QEvceWPdbNbV8MZgOqjFW4VUl+7103dqFM5zijq8pYhWtF19AytCWWjSOFtiWMhP8NFVIFcl7Pwar7auaCSTOhKGb2nMm/pjU/nEFiQzbTcY7lOGhYQtSfm3kOua/n4peHf6lRP+sKM3rOAABklGVU+xh9ovtAJpFh62Nba6tbdY6pnafi8PTDGJEwAhcKLrhtL5Ew2P/mUKx8sodo+91ANjsCJZ2ro2wurhJno1wtugqtUYvbmtvIrcjF8ITheKrrUzXuGwDeQ1jmgmw2mVlcyC2zExM0BuhNemSWZSKzLBO/XyUZuKtTVwMANl3ahMyyTH59WpuwJZsdicqaBTRDu4h2MJgNCFDK+KzLxgof2exDo8AbO95A3OdxCJ8fjuf+eK6+u1Mn4DgOLRdbqy7nK97xmEi400jJsE+/rAusOLkCkQusqo5uzbpBJpGhR/MeiA2Ohb+MWBjMOTCHJ+obC+5fdgQPfHnE4Wd+bBcARBkyMoHYQiQsTkDkgkiHRRLN0PMLwv6x/TEk3kr2VccXzBEqpbuR7f8YNFJiXbL0xFIoJErsu0JI7i6xJIUqJigGcqkcIX4hGBI/hPeZoymUGy9tRNOFTfkAAe0fVU8FKmqvmnGA5Vi/ZfynWvs7iyZTQo5hGLSNsva3fRQhk1/fkIp526xpqlIJg9gw68S5o4fpZiqFjC+4IZMwdeLh3vqdbej4/g6R2hogCuLW72wTKbcOZBwAQMhmpYwspu9d+zjiF8dj7aX5AIDj+b85LBJClUqrDt3kPYPd4XJeObrP2YXOH+xESjp55izYcQUAcKtMh3+sdq5YF86LKblvdKJsTs0qRY+5f2HHBaLSP5tdhs4fWJWfigak2IgNUyFCbVU4VfeaEP5GrS2kc26ZDolvkxTnKoMZHd/fgdl/XECVwYw+LcMxzGL/8tclqyqty4c7UVhpXTTtuZyPzh/sxKxfrEVNaQp035ZWRTYAXL+tgTsUawxQK6S8OlAqYdDdovDefoE8Q6KC/Wql8OTdjjGfH8SAT/bw751Vl/cEtopOHxoepv3vOG87VGEJAB28Voir+ZUolP8XeuklRAU0R7/YfnikI8mA6tKUzD36x/YHACw6ugiTfprEH7NPdB9EqkP5+gP0ng5UBqJNeBtsvuy6Jnw7yxjJMNaAhyc2Gk2CxKpOOu8AqPKVg97EQimToHsLUhSrdVPr2JxWUIldF/NRoTehqSAwRccxW0RZ/EcTXRS8CvST29lChVsyMFo1cV0oK6OIPPtu3NagXGdyOMZUGc2ICFC4LDpIMXLNSHRdYfX0PZF7AiGfhmBN6hq7tv1W9cOtylsYkTACPaIIwfdirxd5RTNA1HbObNyqg+aBzUVBhW3XtrlsL2UcK5tvV+ihYY4hQBaB1uGtERUYJbJva0igdiS/Xf7NTUvn4MBhZMuRaB7Y3H3jBgKZRIb+sf0RHxKPW5W3+PourhAXrra7zhvq71pT0ExNR969rnAi5wQKNAV85gkAhPmHoe3StmiykMzNJrSaYJdR4A1o9iIAXpHvykYj6T/bcCqzFJGBjUN1L8TYH8Yi7vM4xH0ehy1XtwCwrnGO5xxH3OdxiFwQiWtFrgOo3sL2d28W0Mxhu17RvVCoLUSAnwwa/Z21Lq1tNJwVlA8+uIAwMrzq9N1Zc1Jn0uG29jYkIKQUJ8vxOEX6TuNOFS68WiRO67aNdAsHxrqIQNYlLt0qx4VcMcHXL4ZUmvZnu/OLmKmdrWloHDgkZxNF8PhW49ErklSslcuMdkU10l9JR//Y/tVK1RKCqo9MjKUKvCSb39Yxoi/fbuFDXRzuv+fJPUh5NgVKmRJyiRwXb5PiQG/2fxOANbpPlc01mSjZorrKiJVPkAWYM2UzXfhIJQzenWBVa4YHWCdcP57IAgD8Y0C83f7LpnbH5hcGeNUnuVRSp4Ta+RyxCmrfVUIocrCf5GiNWt4/2sQUQMbGINxAUh+bB7RweHyGYfDqSFLokVavdodbpdZJ2Yn0Ejsl7P6rzovvCNvSwia8slkqJjfSi5yTnk/0jbMjSesbwSo5XhhGUiWrG5CkhAwAPD/UPu2SKru/O5oBndEMf4WUt/G4mi+uNJ9TYv09z2SWwhaU0GkXFYQN/+yHJY8SpZ5tcS1HKNEYEKoWkx5KGzXh8LZNfGSzB7hVVoVhbSLRMpLY+XibZSBETb3Xfah7HL1hnRMZTCwyBdkNJgl5vq+dTObTkepIHH36KP438X8ArGqrJceX8PtMajsJGx7aAIDY2aya1hOz7+vAf94qvBUkjOtl5aRupPjvfV2a888uTxyJhOSt0EaIQm9iwXGAUi5FUpNA/DyjH96Z0M7hsQa3isSnkzvh5xn9nJ5vUKtIrHu2D54b3NJpG0cY1ykKK57ogWcGud7P9t7b+dpgjO8cxb+vMpihM5jdKqQpDmYexNWiq3ZezY6K590ouYEIVQQ+GPoBekf3xm9TfsOHwz706DzegGahtg5vzfuAp72UBgBui0mKbDQ4q7KZXMMMogMTar2/tY1wVThkEpldJqI30Jl0HtvINTRQG5as8iyv9msX0Q6hfqF2yvi7BaF+ZB5VUlXi1X40a2T5+OX4333kOa2Wq0Xf78MdHq6lXlohl7i20QCAjyZ1qvXz1jWyy7PRO7o3Vt23ymEWxzuD3gEHDtdLrtfqeSnZ/PW9X2PdA+swse1Eh+0i/CNQqC2EWinz2HauocJHNvvQKOBplevGDI2BEB7BhicwInomDFwZthdPwH//Sq7nntnjTtlo2E6yXE26qnuNlGgMiJ+1FfGztorUqN6g/7zdWLDjssftOY5Dpt9kZPhPADObgWS2BMxsBkezj8LPTKwfeszdBTPL4dcUMRFGvfZe7/c62oeQQWpfyXN47JfHAACbUggBJ0cTXM4KQbnOM2LPGaxWLuRvqWw9pB+SSWAgYy2cplI4nhiG+oeiR3NC3qrkKt6K4dnuzwIA8ivzEbkgEq9sf4X0W1J7ZHNUoFVBzMxm0HRhU4+CEtReoFhjgNZgQs+5f+Hd387zn9O0XQlDiLRRFk9mR9H93vFhdtuC/OToEhtit90VZFIGey4XoN2727Hncr5X+zrDpC+t3pBvbjyLW2XkWvng9wtYsf8GAICDPSGoNWp5BRfH6CHjmiLAPAqBxvuRW5mJwHmBDtUsYzuSxTQlsgHgpxOZaPnvrej24U4s25uGlQeu8/fjP78/ybeTSxnct/QwiuXLcUv5utv/TRiou2gJ6hxOI7/9E6uOo6DcSmS/vsG59/BLw5Psih3WJbZd2wbZhzIsSV7ish0lfs0shzGfH8A7vxEl8ZmsUrR+Zxu6zN6JokrnZGKnGOv1F+gnh+2/KCQTbxRq4C8nHqUAcM4mMLHmKAkGF1XqsWRPmst+904I4y0vHl5x1GVbANhy9havoqSwTbtXK2Uo15nQ6YMdeG5NittjukKVwYz4WVux+UxOjY5TG9CbzEj491aRvysFy3IYumAvPtt5xaNjsSwHo5lD55gQvD2WkHDJN6sfoPUpmxsXTCyH/+60Bu856OFv7ocOTayEbN+Yvrw1GSWbhTZmn478FC2CSTCRYRiMaNdUFAjqEdUDFYYKZJU5J5cYhkFSkwCYzFYKzpPHq/Ced6Q2bvsuqf1AA1G94sOcqpaVMgmm9GqBXg7GZgD49vS3UMxR4IFfO6LC4F1xYZVCitEdmrn1gs4vtz6bmwQqER+hxnRBYHr35Xz8cjrH66ya2ftni97bPiuvFl1FhaECPaJ68GrjiW0nItiv9ot70fn62KSxvKd3YlgiwvzDMPfgXFHxaFtIbJTNBrYcWX6P45f8QTBLbqN389613t+6QJ/oPijUFmLYd8PQbGEzrzMwGzPZnBBCAgLerM3Wn1uPS4WXMCxhWF11q95B74Vh33n3P84/TLIHuzTrwouQhN7rwmPXFm5V3IJEwkAmIZ73m8/k8PNznYAHSHKTydEQYWSNaBPeBtO7Tcf0btMxpcMU0efTuk4DAIz7YRxe3+F+zeEJvkj+gvfWbhPeBo92etTp/R2hioDGqIGfwuhxQe2GCh/Z7EOjgNaoRc/mPQEAQ+Ls/UDvBpToyCREJffH3HteQZJ6AlimHAv27K3nntmjJum33oCqXWf0mIGXer+EucPn2rV5ssuTAKpPNlN/PADYknrL6/05jkNumQ7L9noe/dSZdOAY62JDqHxQsERlWKEzoVJvwoIdVxGpfw+nnrnMq4YiVBEYEDsAAWiPEONTiFFM4Pc/mU7I6Sv5FTCaJNAYa1bZnKrYOZD+BhmnIND4ED4Y8gHaB48FAHz7VC+PjqWSq3jbjBA/QnbdLL2JQm0hTucRcrI2lc33dYnH5KRX+fcFmgKPVBZ0AV2sMSC3tAqFlXp8f8yaXcHyZDNZEL09rh3+NaYtBibZe29RBbAjbPhnP2x9eaBH/4tMIoGJ5VBlNOO0A/VodWB7nLQC8gxafSSd3+aMbA5VKzDvgU7goIcESkwfkID/DHkVL/d5GZWGSpzNP2u3H1Wn6QTPj9l/XATLEcuSBTuuYP52K3kmJLSigv1xLqcMFbKtMEiuOlRcCyFMCqlw4HdG/ZmNZhZmlkPzYPsJ39z7O6LJHfYCTslNgZkz40iWY4sdCnrtmVgOl/Mq+GKUF3LLYDCxKKsyOlVsx4T6Y0KnKNG2dc+SLAVawMu+8JUcQYJreVq/OCx+hKRtVxnJRPiGxUPZnVJRLvWcvFfKJSKPVUegHtwVOhN2XqxZIKZIQ55zn2zzPHhYVyirMoLjgI+2XrT7zMiySC/SuiX3he0BQCGToEM0sTKoyQLGp2xuXDCaWdwUepwHMxjaKsZp+17R4jF90ehFSApLcnmOthFtAdhnpNmCEhesF57NQmQUWed61LqKwjbrgWJqH2vGja3Psi2Sc5JhZI3I1+Qju9w+0CPErtcGY3SHptg0sz+WPtYNIU68S3+e0Q+fTu6EKT1j7T6jvvRqQSHB1CwyNlPrIlcQWqXRWhgUtkpzanOy4/oOt8etKUYljsJnoz7D24PeFm3/+t6vAZCxzhlsCwRWsdlgmVKozIMQbHwE7wx91em+DQkRqgjc1tzGvvR9yNfkI6PUO//mxkw208xCb9Zmx3OOAwDeHvi2m5aNF92juiMmKAZVpiqvbA45cPCX+SPMP8ypKKc21k87H9+JSBWxr6RZ5TIpA6OZw2eCItI0M61/YsPK/PMURrNR9D0uuGcBbysEkEKLS8YsQWJYotv5uKegxS8B1+I5wBrwlckqUdnIi2H7yGYfGiQu3b6Evt/0xebLm9H1q644nnMcTdRNMCB2wF2bWrPzClHxDW8Ti75xbZCkIlG2fOW/7YoC1DcOXiPq2Sd/fRKfHPoEADDv4Dx0X9FdNDlfcHgBuq3ohvTSdI+Ou+v6LnT8siO2XiXFMPRmPUL8QrB8wnIsGbsEfWP6Iqe0Cj3m7OKjqztPtAYA/HLJ8yIhs/+4wO8/bslBfntOaRWGL9yH1YdvenysTaesCriFO+xVZizLYdKXhxE/ayvWHyeEkKvJlx9rTUsdsoAEGlRsb/yWYsR9be4DQCZiSpkS5ToGwaYHIa14kN/HYCKLneQbxWA4OVjOnmij9xe1tACIsn7A/wbgxT9fFLWlJAXL6CHhghBqegKhpifw/tD3IeFCEBeu8mhBBIhtLaiS5odzP4ja1KaymWEYbJy6SLTNk4lvoGXR99Gfl7D/qn0BrGUWgocSfvERaswcmihSM3HgUKCYjalb+qHN0jbot6of/vnHP0UR8t4JYTiWtxGdl3dGap5zdS3gHUFXXaxLzsQrP4oVyRzI9SNctH596msMWT0Eg9pKYJRkguEUeO/e9vjXPUPw4VCSjvvU5qfQZmkbtFnaBl+lfEWOIWGQEKHGplNWdYTWJnDlqCiqCcV4ZPNQZPpN5rdVSl17PgrtJaoMLF/UjmLy8qOIn7UVrf5DjjPDxkqiR1woHu8b5/IctY2ssiy8t+89AHCp+gKsi3FbG43Vh9P511NWHBMVOqR4fmiSXdHDvi3DERXsB43BjIJyHU8mdrPYbfjJJSIC4Il+cZjYNRqdY4JRZTDjan4FHvqKKJX/eHEgxnRw7EMHiMmef/9yDsv3iQN1RjOLqd8cw+YzOTCYWHRt4ToLwNaDuyagX+etMh1S0otxLrsMoxcd8FhBXJugv0GJ1sgXeNx1MR8zvj/ptcUWPZZCKkGohRCrMrKiz6esOIoT6Z7NNVYeuOHV+X24s9DZZJ/RoLFGug8Z/hOQVXETMSHO7yvhPPvsjLN4te+rbknhzk1JYb6ZW2di5paZTttdzqvAzov5GPgpmd94G7gQFvWLsgkSOlMzCwPBrp4XqXmpWHFyBf/e3XyhVdNArHiiJ3rEhWJCZ+e+ur3iwzClVwtM7evYZgoAVHLrd/71QTIHvceSNeUMWqMWnZd35t+X68XWbJsubsL0zdORnJ2Mfqv68bUyGNT9fMJP5ofX+r1mp7Z8oN0DCPELwcIjC53uK/yJ0goqkWn+FgAQZLoXTfAkWke4Dnw0FESoInCuwFq/oOPyjqgyus82LNeXY/C3g5FZlgk/aeMmm/el7/N4n8KqQiSEJPDZkHcjpBIpbyNYoa9w0xp4Z8876PV1L2SVZeH+tvcDEAfopnedXqv9uyfxHvz44I8AwNswyqUSGEys6NlJbTV6WrLsGhsMZoOInI8NjkXKc9YAGMMweKnPS+gd3RvJOcnYeHFjrZ7fU7L5UNFcXL9dieEL92HZXs/EBQ0NPrLZhwaJNalrkJyTjHf3vovU/FTck3gPXuj1AgKVgXaTqbsF3x8nafpZheQB9K8RIyBnycS0tg3qawrqI/f92e/x793/BgB8dPAjnM47jVO3TvHtPtj/Ac7knXFLpFHsurELF25f4FUXepMeSqnYmuBcdilf9AYA5GZCNudrPFe07Tif53D74NaRKNYaXHrB2uLIdSsZudTBQFCiNfAK0vnbiVrOVSodA+v/WyooUPfd0Qy83vd1TOsyjfd4KtEYEOQnQ/dYq1qGNZMFC1lsysHCnmz65dIvSM5Jxtqza/ltmWWZOJJ1BMtOLBO1pYtUFpWQcESZOq4TIZKqjGb4e+gpCACzBs7CIx0fweyhs6GQKjCjx4Jh69AAACAASURBVAz+sxC/EMzoMQOdmta+99f3k77HnGFzAFjtalxBOJGbs8VeVbjCQrSolc7/dw46VElPINgvAE3VTXEs+xhWnlqJRcfE5Pc3p7/BuYJzOJHrvNgdAHRvYZ3QOSrUUR3EhhFbhAkWr8jCSj02nxFXiecYcq+9P+R9PN/zecwZNgcdm3TEgYwD+PMaKSY3qZNVBRektCrNjGYjCjQF2J62nd/2/NBEh56bFN0dEIsGyXVUsldF2QB6iWvlKctxPDlaZTTjfK7rcSNMrcCciR14/+L68MtPzbc+J02sayUD/d9s/fOpIjkuXAUTyyHTogIUZk+EqR0HdGiWx6nMEp4AmtilOab0jMXk7kQF+eHEDniqfzwSIsizwE8uRZXRjJR04j84qn1TtG4aiDn3d3Tad6H6cP3xTHy6Xfxb5pXpcDitCP/adBZ6E+vwGbPtlUH8a3dKRW8g9CX861IBkm8W4Up+Be/BfichJAzPZpN0/mfXpGD7hTzojN4RdDzZLJPw37/QDiuzWIvkm8V4a6N9RoIj0MCpDw0TF20KvlK/+kKFleCb1mWay2MsH78cL/Z6Ee0iHXsf24Iujq8VX8NXJ79y2576Ftv21R2kTkjvnnGhGNjKPrsIED8jRrRzHhynBZjHtRoHoPYt/ITqZQpaI8JPbv8ccze/OpJ1BFeKxIGwrs26Qi6R47+j/osWwS3w4/kf8enhT3Es+xiyK4gYZMtjW6r7L9QKmqqbuiyQJoyhLt59DQaOWG8puESn6vWGCKoQFcJWfe4I29O242AmEcI0VmUzfR7Q4t+eoFBbyO93N4MWQq8wuCebPzr4EVJyU1CiK3FYh+bp7k/js1Gf4bNRn9Va/2hxUKq8VkglMLEsOjS3zu8rLGpbWQMqoO0NjKzRobjpi7FfIHWGdS7+XPfnAEC0jqkpnu/5PJ8J5AxD4kkWf6nxKsZ1ioLGYMLOC465i4aOxnmF+HBXY9HRRXaEzIoJKzCu1TgEKu5esplhyMLPjyED7dA2UWilID62DcGz2owS3FZ8jErpXhy5XoS4WeLJKh2cPtxPlI1Xi67y/XbU/2PZx/DghgeRXZ6NxccWY+ovU7Hp0iYAwKZLm8DMZrDh4ga7ithHrot9JiVQITqgJVadXoX39r7n8n/gOA73/3g/jhpGIV/xPvIV76NAMRd6Jg1F8uVY+lgnJEYG4ExWKZ75LsUjldfuSwWi998cvIEjaYSAXr7vOnp/vJv/LMPwEwauugfj1hLVeoThTbvjMZzSKRk3IHYgVt+/Gi1DW4JlOZzLKcPI9k3x8nBrkTq9hVTQGc1gIAPLmcByLH6+8DPG/jAWE9ZNwPnbJLBBJ7MAoDFYJ/4vbH3BWhjQTJXN5VAwwUiIUENmKRhx6FohlF6Qzc90fwbrJ6/He0PI77RojPU+3/TwJiyfsLxOJtaPd36cXzzO2j0LU3+Z6lRdkpqXinvX3wuTepPdZ29tTMXhNGtwwZmKCrDajjzX41n8Z9B/RJ/RIo+5Fbl8xWd39/h9Xa2qqa/2X8evp12n9wqhM5rx+k9ncCZLbJsRG6pCz7hQLH2sO8Z1aoYT6eKCJaWyH5CvIOmM7SPbY9n4ZXhn8Dv875dZRgin5/tN4vdhGIYnMUYnjkbbiLai/+2hnrH44lGr1zdFk7A8FChmY3veGzAy1myB4R31uK2w2ue81f8tdGnaBU1DyHX5fz+nYuke+2CcmeWgsijN52y5iJfXu66IHuwvxxP94tErgfh43kGbZh50bPtH13/AyBpdBkYo4WKrbDaZOUzuHoO5FrK3ymhGscaAj/+0ErrOUr0pZqw9hYnLiJ93y8gAfPpgZ7S0+KQ+2S8eH9zXgSe7/eVSHLtRjLd/JeqtJY92g0ImQUSA83M4UhayFnL/55QsvPbTGQDgCVVHhEs7Qfq8beFIndGM1Ydv4oPfL9h95g5C+xCd0cz73BZU6PlnYU1x6VY5Znx/0k5tb4sqg/V8xVpxuq2tH/ecLRfx/ubzeH/zeRRU2JM49P+SSyVgGAb+cil0RjM2pGRh0Pw9mLXJM5LZh8YBP5uxyWjmYGCsKfwfDPkAA1q4LlI7o+cMfDHuC4+zCcP8HXsgu4PeS2Wzo+wXAFg0pSuah5AAaoGmAA9ueBAztswAx3EisjkuXG2374GMAxi/bjxe3EYyu2gaf23PvQMdkM0j2xH1siPSxp33My3CB1iJzd7Ne8PwrgGv93sdT3Z5ElWmKuy+Seah1JquQ2QH+4PdQTza8VFw4DDq+1F80FoI4TURoJSBhRFq03AwkLucdzU09InpAwB4qP1D/Lb/Z+86A6K42u6Z2b70DgpIRxHsIhbsvRs1xsQSk5i8Gks0iaa8iYlGX1PURNNjEktMNPYYey+oWLCBICKCgPQOC1vn+zE7szNbaFL9PH9gp+ze3Z29c++55zmHq5w3h+j0aHx2zjDmaalkM0EQiPCMwInkE5j09yTklOdUefy6S+twJOkIrMSmv8+nDYwooybKZi7Mkc29vHphUc9FWNRzUb20DQAr8mIC3EUCEnfSi3lilNEb6IU548DtloDo9GgUVRZBS5na8c0Ln8dW6QA06RvmGsbmF9zNvYuJf0/kVQbXBIwyekmvJfhu1HfVWp44yhyxOGIxtJQK66d2Rk8/J9xKL2b5hZaEZ2TzMzQ7fB39NdvBMX+ZjtlB6oCiyvrxK21uCHKnbyILBhlUJBIBPXBuDmSzkoyHQnARRaJN+i381WoHGa28vJV9CxRF8VYBzbV/w5UN2B2/G2dSzuCto2/hzzt/IrmQVowy33dRZRGmd5jOO6+4wvC6nfQhaxJ9mdmKcyuqJBcUagX239sPQActUQAtkY8KwWVkSRajTHgQB+/vg1hAolChxon4bOy7UX1IlHFY4mcH4/HiRppM/C3qIU8hWSI4iJtZ15GUWwiJNhRiXTC77/XOiyHVdoaI8kRXbwd09zEtTcorV5r8LyJJdPC0R1vrqbDRjGIJmgq1FgTom5lKq8LmW5txPvU8Dicdxt74vQD4E5UHeYbf1ffXvmdX3Mv0vp46ogxi0gZCkmBVUoCBJKoLuINoYwV7fSPIKQjD/IehVFmKP+/8iYQ888rYQ/cP4d/Ef5FHbDfZ9/e1dEz/1aBKqcqrjFHhykVykwHivoR9AICdcTvZbdX9xo0Jt0U7alYtAADJueXYcyMDqw7yAzBVGh07CR8VZloCrJT+Ay1JD2y43xUzqWXIZuP3xzxm3rvxexMLSUzo3Jq3rbVHHCoEV1EhiIZaZHhv+dpogDD8xmQiGWwltijRD9J3XU/HV8cSTZTIOopeDIi0oHQzRofWdF/SvpUtegc4Yd7Axi/TZSYeTAhXVQurDNlr7K2s0ekgEhDs9VKp1uJzjv+wWEAipBXf55QBo7Djoo2T6eSGC6bKgQFT9UIQBF7u5YPtr0eYnONhJ0P/YBde+FWunjzdfjUN97L4E7Ce1XgCnjOyuknOLccnB+5i08WUWhNZ3JL+RwUKXv/O9fd/EuyJSceRuCyThUpjcF+7UF/Nw6gfGVUoQKuxf73wEJsvpWLzpVR8vC/O5LkYf2amGkMsJKFUa7Fk122kFVTgWmqhyTlVgfnuXM2Eoj5D04O5Rw8IdmEfKwTn2P2MJVd9QkgK4WZVte0DYAg3ZVBZwwyQ9VM7Y+nwtnh/ZFsMbueGgW1d8clYA2nKXcSKehSF3fG78dP1n1BQUWBWUczF9tjtLOnpaevJ2nzV99jbyVqCkWHuCHazwcu9fDA70pe1NHKQi0w8nb0dq+5/mfmQh7UHVg5ciQjPCLbyDQD6eNOZEMy9hCH8bCQ29fOG6gimuu948nH8FfuXyX6lxnBNDA1xgw4qEKAXMFuSsrm3V28MDxiOJb2XsNuqq/TcdXcX4nINfXiIS0gVRzdvTO8wHR7WHtgdvxtRj6KqPHbxMdpiLrusfgKwmzOY319NxHPMIl6EZwQrmgGA9/u8j4MvHmyQ9kmEerJZb6MhFBC4lW4+LFVEtpzfI4N3jr8DACZVIZbgYuXCks07YndgT/we7L5rKkiqChtjNgIARgWNqvE5zPyJoiiM0OesHLhd+2yppsbTaX77DC0W51PP41HxI7wU9hK23dnGBo1YieiVTme5M7LLsxGXE4f2rk27Ml9brL20FpHekSbBKwwo0JNBXycDESAVygAl8MedP2rVQdUnKIrCb1Ep0BEVvHaWCA+wxxxJOsISxRqdBnMPzkVMlsFOY8vtLcivyGfJKcAQVHI25Szv9RaEL8DMTjPR9eeu8LX3xcpBK9l95UoNb2V1zfMdMWjNWYhIGbtNoVZYXBnnThw+6L4VG6PuI0M6CyDoiVlBRQFiio5Ci6EQwA47r6XDyVqCeQMCzJZqFylUJn6DFFQoEv6FhYeP4aE6DRJiFESUOxI/GwHZZ5UIcxyOzNTpJs81zvcdHL04EADg5SjHoiFB8H2fnvx892IXvPlnDP69lQlriRAkSSDCjx6AdG3jAEcrMeLf/hP/3XcHf1x+hPRCBa4+LACh7+KVGiXK1eXo4tEFSQVJyCyjb1bc0IN72bSKW6btiQrBJURsjMBA34FQVIpAoR90qIBEYAWhgIRaSyGvTIlylRZ9g56s5C3MNQx3cu6YKNjrG9ZiaxyZdgTHHhzDsD+G8a6F5MJkrI9eD41Og8NJtIevUluBfNG3ICCEMzESlWo1KgQ3YKuZCAIEhrd3R3bFfay48DPcrN3wYeSHPPuNI2/1QNiPgEwog0wk47Vl251tuJxxmVdGuuLcCizuuRhigRgbYzZCKpRicshkLDm+BFpKC5WGQJmAhIp8AJm2O2S6bqAoqkofzejkfCRml7LkYiFHHbn1cipS8ssR2pqeVI/q4IH39gjZ0rjJXVtjbbxB/c0lm5kyR8aLz5hslgll7Ha5SI7CClMia92UTpjYxRMv/XoZMudt2JdsIPdJgl5Q0qEShx59DQIC2Kono1hEH2MjsUGe+jyUoh9AUvaw00xBcYUajlaGa0inoyAggR+mdUXosqrDkFJWG/pWVxsptr1mSpA2BpgFHoawqYroYC1COETN9dQCZJcoIRQQLOn724WHOH3PYAuUuHKExeccasZnuTqyY0p3b6w/mYSMItNKAS4RxIVYSGLTrHAAwOE7mZizLQbfnkqCQqXF9dRCDG7nioyiSsRnluDHaV3RwbNqz2Zj9R+XpN1/MwOJ2WVQa3WwkQqxcFAQRAICb++8BTdbKZYO55cy3uZMqBhCt4evI6IfFpioyOuCMqWG9WPddDEFs/v6WTxWoTJYqVxIysOCQYHsYuLpewai+gsjGxLjBQgA2KPPFmB+IyIBgfP3TRUy3BC5qhDu64gLSXn16pf9DPUHZpFlaHt3nL6Xi8dFldDBsEDR2aNzg7zusn7LMPfQXAD6ACYz6q3Zkb64zlnc4AoIqsLYjoYF0Y0zu5ns5yrsvr36Lfu/UquEo5y+z1kiKo89OMb+/8XgL9h7Wk1st2oDAUng+5fM+9ESBIHPJ3XAjmsGyx6phcoxHaXD8rPL2cyLG2/cgJu1G2Z3nc07rrdXb4gFYrYcPiqNJvyYgOamQoXGcL9gCC0uuDZBYiEJCipWPCExYzfSXOFi5YLDL/HzJeLz4lGqLDVL+J9PPY+vLtFWNyRBQkfpMCZ4TKO0tSEwt/tcjA0eC691XtgdvxsT2k0we9y9PAPpV58h4c0VjKBq251tVVaYXMm4goKKAiwIX4BvRnzD27dq0KoGax8zH2MEf2Kj+/zc/v74Xp+10RKUzWqtGp+e/RRFlUVwkjmx3vU18U8H6DnPieQTmHdoHms1eSn9Uo3OPZl8ElN2TYGQFGJk4Ej0bdO3xu2Wi+SgQKFYWYxh7d3h5SiDUl2zxdnmhGdk8zM0K3we9TkAYIDPAF5wGEOoMJ3yz9d/Nul4mzNKlCV4+9jbaG3TGumLzZe/qzT0gFsuNiiFbET0Stb51PNmz2kMxDwqxIp/74IS0J0yAXrwWyTczB7z6dlPAQAzOs7A8QfHsfMurdgc4jcEhZWFuPDoAs6lnoOQFMJOYocSZQnr45Wj4Ku7AhwDEOAYgLbObfFxX74tBtfWYkiIGztxEBCGwUmxsrhGZLNUKMPYMF/8wKnA33p7KxIromEvBOw0k6HS6rD+5H30C3JG1zamJaKxGaar0koyASWinVh/BQAB2AmsMML7Tf2AWQkBwS+JG9hqDqys0zF3m4GcD3a3AUEQ6Ohph0ndvFiy8HBsJmt1cOpt2s+JS4K3b0VPqA7fyYKWokBQ9Oei1CqhUCvgIHXAsIBh2H13P0pV9PMwE8LsElppogNNtsTnxeNh0UNUairhRrqBIirQvU1rEEUENFodXt9CBylkl5hOFGqD5QOWY+mJpfB18H2i56kpzCVk/3XnL3wT/Q0kAgk7uAKAMuERgCLQ1sUKN3MuQk2mwUrTH0I4Y3rPNvjp+ifswGNah2nwsffBW4MDEZ1cgDI1TRzaSGzg5+AHfwd/UKCQUZKBtJI0pJWksaQs056rGVfR27s3Zh+gJ4w55TlYf2W9ofF6LlVJ3odM2Q2ZxZVs2bA5zPjtCpQaHba8Es7bTlEUlu2PhVhI8sI9uAFwtnICOkqHNnZ0SF6QUxC7z93aHV08uiC1KBUhLiFoZcNXRUd4RsBZ7ozw1uFILEi0SJqGedpBLEvEvXKaRJZpw1EhuAJSoK9sIROg0angbRsAm4rusJFFoY93H1aVXio4BBAUrLR9UVppRDZTFAQEASuxAEFu1kjMNm9ZYDyIbkqUKEtAgICLFa1GrIpsZkqJub7uE3+gB79CkoSXoxy+zlYm1inVYXakL0uG9vJ3qjYUDABe6O6FNccTEehqXavXAsD2bVsvG0r8c0qVeLlXG6w/mYQQD/MqbACIDHRGZy973M/hf7dcr+Olu+/w9g0NcYdQQLDk62t9fOFkbbjnxum9vV1sJCzZbK1XRdaHjze3BDKjqALFCjXs5OYn19z3UaIn5MQCEiqtjs0BAAxhYgzM2Qyc0S84BLvRBIeQJJFcQ2LZHJjPwhyx/QxND6WerGMUvV8evQdKRPcniyPeabDXzSg1VIMp1ArYCexMjuH2WQCwdESwyTF1AVdhxxUxKDVKeNhJ0dbdBjN7+Zg9l1EIt7Zpjc4enc2OExoLX0zqUK13enxuPDvuBixbmBAEAWe5Mx6XGkQavva+vMDfpsDU0Kk4+uAoHhU/4o25GPTwNbwfjY6ilc0Uo2xuOTYaXGwYsQHzD88HAJx6eIqnQGfAzIEB4I8Jf2DFuRW8HIyWCGbx3FI1IQD8duM39n/jed/TiABHunLuePLxKo9bH02P/yM8G1cAwQj8mAoE7qLymwP8MbmrF0s212dmRkPhdvZtrDy/kp3jMYtt7/Sq2b1wgM8AnHp4Cltvb2W3VZW9xMXgrYMB0J/pQJ+BtWo3Q/rH5sSij3cfyPQZKS0Nzf8KeYb/N8gszcTB+wcx1H8oJrefbPaYkYEj4e/gj7yKluVZwygYuQPx+9ml2BOTjqScUmw8n4wLD+iVNrnIMPGVi6wRZPUcq0poChxMOA8FeRE6gh5061CBYuFuVg1MgMDl9MuYGjoVm8dvxuO3HyNvSR7yluTh2PRjuDr7KlsGNiJgBPKW5GFK6BT2+eNy+CW/L4a9CFuJLeLfjMfUsKm8fYxa59/5ffDLjG7sTa5v6xc4x5gnP+Ny4vDjNUNoTYRvK6yZzCfhbmbRXqFlgiOgYJhEKy2EMRknvgOAQnCR97hTGwH+nN0D3135DhRRiXtZ/O+SKJ2CvyftZR+vn9qZVfLtn9cH0yPawNfZCoPbufI8dRV6RSN3EDCpKx3ipdRo9QErNIlxNeMqrmRcgVwkR4h0KRyLt8JB/SoAIDaTJiEeldHKApI0vNfl/ZfrP4+T0BIF8HNy1NtoUEjRB489aUnj+LbjcW/evUYLBWEmkYwSHwDK1eUQkkIsijD1PGvn0hZq4T2oSVptVCo8CA2Rg6t5f+Dn6z+zx608txLbbm9Dl8AsLBpF4k42TXK5yF3gKHNE0oIkPFjwAK1tDfYRXw39CtQyCtdfvw4A2HRzE9ZdMvhYM/6KxlCRidCiFDuvpSMppwzxFgKWmN/LrE10+GCS3iM2KacMOgpYMCgQ8wYGssdzg5esJPT1tShiEVLeSmGtHQBaeXL99evIW5KHuLlxJos7k9tPRu67uRgTPMasjQYDO5kIa18wvL6L6iOAIpCj+wc6lIMC3W/ue+FvxH6wGGmLHmGw32BYi2lSU0zRA3YtUWISmqjVq74JgsCxRf2QsnoUS7r3DnDC1HC6XPnjMU1fonrt8TWsu7QO5x+dh43Ehh3oV0V0MMT67hjTxUshScBOJsLpd/rjxsdDa9WWD0eFIGX1KKSsHoU/Z9dsgjN/UCBSVo/C8cX9avVaAO2funBQIG+bjqIwpbs3ot4bCO8qbDy2vtoDi4cGmyiOD9x6bOEMYFt0KsqVhn770B1+SaJGq2OJqawS+vqzlgrZdj0pfoviE8PccnFjPMyjv/9wH0ck6K1FGGK6pNKyGvScmYDbSo0Wozt4wNWWXuysSo1k7r7GxbG4LFaZmluq5Fl6cHEzrQjRyflm9z1D/aJIocK5xFwcjcsCRVE4HEtf11yPYApqOEo8sWbYlw3WDm6fZan/Yqy5GAxsW731Rk0gFBDYdnsbdt/dDS2lRaAj3a9su7MNUpEAR97qi6nhhvvYrru78Nedv1BcWYz8inx8GPkh0heno61zW3ac8OuNX2vt+/6keN7ISsMcjFV1ValBjcdW2yeZWoQ1Nnp798b9+ffR0a2j2fmNk7UE1/9LkzQarQ4U1C3SRoOLeeHzkPoWvai6Pc78d8DNUZkaNhV337zb5AsDTwqRQIRJIZN4anYuylXl+P7a92hl0wrUMgoTQyY2cgsbH85yZ8ztNhdJBUlYdGQRr8qUi3J1OTq6dTSZCzc0nOS0dVm+gr5/M+OFjl72eHdYW/g4W7GVJvJqfOWbAmdSzuDXmF/x3ZXvkKfIQ7maXlif020OAHpxcYjfEDzX7rkaPd9/uv0Hue/m4vKrl9lttQm+BIC/J/+Nt3u9Xatzuraiq2CYuaBMJMDh2CxekHVLQMvuwZ7hqcK3V+iytzDXMF5CKOM5xsBB5oCCiuqD25oTzBGgb2y9jsV/38KcP2Lw2cF4lKvoAZdMZFDniQUkQEma1LN52eUJyJWsgpaglR8UUYEi0e8ARUCoawUK9EDcz8FyOTAzcGcGvdzB04PCB+z/DlKHKsv7GMsKZrDJKBzcZQbVpTmVBAB8cOoDrI5azT62kUogFUp5JBpzrobMhppIYbdbWklktnPVd+WCM/xjNMU4kHiADZ6h1B68/Uk5ZTjKSZjt0NpUCQSAJRsYMBN87qqykPFx1eig1VEQUvQkbvRfowEAjpI2+O40/XkTFL2o8ekBOjjtfhF9E7XRjGafz1MeASFhhXLhCQBAXG4cJEI6WKqM9QBtWQUynrY0Ic9Y9AD0pFgukiPMLQwAEOoaCrFAjE7undDBrQO7CAEAJaKdKBRuxqKji3iDjY03NmLa3mnot6kfIjZGsJ57xoptbgIx433sY+8Da7E1Nt7YyPrWAYCrlSv7f2d3uuxZqO8bFYKzWHciEYPXnsWIb6qufGBUiMzCxJB1tHensQ90F47K2dOR3mcukKQ2sBJZVdl/cSeaBAgIKTdoiSKE+N8BOL7XXLRzpn3t/WxoSyIdSnk+4gBjo8En1IL0qs6XerTBmA70QJmxo2lKLDi8AIuPLcaZlDM8oqOqz83Bir4Orpvx2hUYEYlt3en33dHTfN/S1GDax2BIO1M7j6owsYsn+79URGL71TSTY5jAwr+vpeN3DuG79ngi7ziVlvYxt+b0a0ygmKVgspqCoihcTuaPXarylH6styZxsaX76oyiCvYeWJ31gHGYYaVKy/u9V2V/UVLFc6s0Ory+9TpP0bzyoPmgnPHfRWHKz5fN7nuG+sXsLdcw47creGPrdXx7KgnbomnLMise2ayBrdRyJUx9YHzb8ez/lvqvCD+DB/tzXVqbPaY2eKW3L2ykQmSVpWHa3mmYtHMSAEOOyEenPzKxciqsKMTknZPx4p4X8dVF2raAqeIBDMq+65nXkVSQ9MRtrC26eNtbDIoG+LYf4a3DLR4HgBd2BRjeW3OARCixKBBhgqgr1fR+1kajhZLNgEHluz12u2mwraaS9fAdF2yqem7JqEp0sC9hH8pUZfCyrX6R5WmChw09F/w6+mv0/s28lQYzN2lsMBZ4jE+xjzPdZ7R1M4zVhoS4gSD4Yc3NBQM2D8BrB17DvMPzsDFmI3vtMVWDAEzsDWsCT1tP1lLQkhjIEjq6daz16zFcGDPXZGyVuFXeLQEtt8d+hhaL4spiXE43TEAoisLZlLNYdWEVbCW2+HLIl7xV+jMzz/DOtxJZIa04DUeSjjSp4rc24LZzf8J+/HXzOJLy6M7ifk4Zwn1tUUHS6kOud61YSIIATTYz7ze1KBX7E/bj8P3DFgdptYVGp8HRpKPYE78H+xL24cC9A5wSEXpAVEneBkFZYUO/WyhaWoSXfc7CU2rwHuIGYBiDsQtgyGam4/9h1A8oWlqEkvdKUPFhBbLfyYaAtLxKyqzmMRNlZtDpIgvE9om0UsDSNVGiLEEvr17YO+4BvCv2QSYSgCAIJM1PYtuQ+65BEaYjFNj/Jj0A2B2TblbdwpDNP03vioQVwxDmlwYdymGrfg6fdL0Goc4TtnIdDiYaQhystXQZzbD2blg+jvY0/esKPTGM/mAQe1M3BhPEN28AreY8d59uK5dsJggCYiEJlZaCRkdBpuvEWmmIBWIM9zQQmQRoAiOn4j6WHfsdyYUpEOsCIdP0R4DqILwr9sBZEoSBtvsxyPlrAPT35mglRkG5PAcTEAAAIABJREFUwavamNBr7nC1coWTzAm74nchuywbCrUC+xL2QSaU4cWwF1H6filuvnETJe+V4Orsq/hz4p8oWlqEv8ckwFFFe1FqCVO1nru1gSCjQGHn3Z1wtXI1URX988I/IEB/Zox3l6PMEXnv5qFoaRGr2gXAszC4MvsKit8rRsES+rV1RO37PpVGh7jHBk9aY7L5p2ldcXf5MMQvH47ObQwhf08CuUiOUlUpDtw7gCNJR0zCX4xVOx5K2pbEySkZYcHXzbbh7V5vo/yDcmwYR1/PFKGERktBq6OQkEVP1nQUYHxputtJkbJ6FEaGeaBXgDNSVo9CgKv5oKTkwmQUV5oPRKlvlChLMCZoDIqWFiHqlSh2EKxQK5BRksG7XzKoqpTYOLTz4IJIxH46DHvmWvYGbEqMCPNA3KfDkPjZCCSsGI6FgwOrP4mDoe3d8WDVSNxfOQI3Px6K258MReyn9HXM9I+fjQ9jj3+sD/rzdbZCoULNU4motTqIBCRL0tnJROxi4pPaaMRwrC++ntKJfT0uKIrCxaQ8ZBRVgCRoZerIUHpSej21kD2+pIKvDvU1um9wiXGtjsLj4kqet7WQ8+MwtpJRanS49CAfRQrTPsacoobrCc6A+R0y7+kZGhZcm6C0QgOpw100AaGGjYRv41Xf6NumL3ZN3gXAYE3xoOABLwgrtLUdWz2x9vlOT/yaH41uh5iPhqBUze+vHaSGxdNtd7bhn3v/sKQzN2j8dMppAMD0joYsDQEpwM7JtB0c43PcmNj5n17Y8koPi/sZ5Xb5B+W4+Ip5VSSDzeM3o/g9w2fTFOSVJRhbl3HBLJoylaGsjYYFH+uWAIlQgs8GfAYA2Hl3J/Yn7GeD81KKUgAAP476Efte2NdUTWwQyIVypBSlsEpZLhhVb0OF3TVXzO7C91fniloUagVOJJ9AbE5sk/1enWROrNJ+wwudcfuToVg90TCOGtOxFRI/G4G27o1HNl9Ov4xtt7dhf8J+E/6jqLII/9z7B6lFqSbnMLZKjMAHAN7pWXs7KRuJDUreK8G44HE1VjaHuobiuXbP8apaawqGC2OI7RXjQwEAeWUtg/ti8IxsfoZGx7zD89Dz157IKKEtJRLyEtB/c38Aes9KgoCAoAcTbezamJCPcpEccblxGLFtBHbd3dWoba8ruIOp8TvG48X9Q5EvMpTLlxHRUApoJaSQNEwOxAISQsoBFCiM2DYCe+P34uX9L2P8jvEY+edI1hv5SXHg3gEM3zYcE/+eiAk7JmDs9rH45jLfE1tNpgDQIcKHTuqWieSgOF0IV41uDDdrejXf155WeTLKxADHANhJ7WAjsYFUKK02GIIhOBkCgZkkK9U6lqSzRMAr1ArYSmyh04lBQMhOvEUCEdsGLjHoaA14OtCkz6E7WWaTeJlyY7lYgNjcm/g3cw5AUBBSrfH7hSyQkKFCW4SfY2i7BSEMxJajlQROVjThy6jduJ6zxhjXmb5R9dArMX+PSgEAOBmdIxaQrLIZAFt66CJ3wT5OuCKjbL5cshTLL70CNfkQIh2tEFRrKRAQo7BcBY1WgNbWtKfi2OCxsJeLeJ6L4b5NrwytLQKdAvGo+BEWH1uMH6/9iNTiVJYsthZbQ0AKIBFKICSFIAkSdlI7yIW2ICl6UCWVmCo05ofP5z1+XPqYTX7nQkAK8EbXNwDwfRYlQgnspHY8FdKlNEOprJAUwlZiy/mN8Mmm7JJKVAVGfTBq/QV2m3GwGkkSkIvp3wazIPSkA113a3eotCqM3T4WI7aNYFVnDIwHbCQkEOhc8OedP3Hq4SkA5sOM5CI5WtnSSl0KSqi1Ovx49gGGf30esRnF0FIUyCdYCPFf749+m2pvC1EXKNQK2EntYCe1g5AU8pTNw7cNR89feyK33JTQswRjIlJAErCWCJv1wpCVRAixkLQYiFUdBCQBkYA+31YqgrWEvo6ndqfVUm62EpNzmD7yy6OGcCKVRgexwKBstpYIWWL2ScnmaRuj2f+Ze5ix5/Gt9GK8uDEa03+NhkZHQSIiIRPTxy746wZ7DzSutrGT8e+dXMuP6If0BJ/77Qs5BHM3HwdwEfOoEFN/uYz3jPyuAUCjNf0MSs1Yegz/2lBt0RI9BlsaRJxqBq5qnUs266CGRGj6O6hvMMFnjNVDwIYARP4e2WCvRxD0b59LaAPgKSXnH56PcdvHsT7H3GOj0qLgYe3BC8EFDPedWftn4VbWrYZqvlkISKLK/lqpUcJGYgO5SF6lQAOgF3S5vr/NiWyWCqUWQ7qYfrdCqyeb9WPZSlXL7k+Cnenx9JRdUzB+x3j0+b0P0kvSsfkmnYNTVZVoSwUzvv7g5Acm+/Ym0DaClnzHn1YwVhUMIjZGQKOjx25fRn2JIVuH4HHpY4v2I42Bq4+vokJdAZIkYCsVmWR4NGZAcJ4iDz1/7Ylpe6dh/I7x+OP2H7z9K8+txLjt4xD6Qyhv+/57+7E6ajUIEGz1KgC0sW+DukAkEEEkENVY2fwk6nRjZTPDEZgTAjRnPCObn6HRwag8mTJ2riXG6CC6hJ8gCGS+nYkbb9wwOZ/7o20pdhoMAfpBn//CVkITJJUC+r1RUKNUR38WF2fF8DpzsZCEjWYE9kymS+bu5T5GcWUxQpzpDpOrzqgpSpWl0OoMg7U8RR6uZFzhHUMSJKLSohCbE8vbPshrGrr50AMCAUlCyelrGRIsp7QSChWf7Ph93O+4/Z/beKMbTbJ93O9jJM5LxCDfQTVud2G5ig1FYibqJEmwgUnMJIrxZgJoRQQTjJJalAqZUIYkfZiUo9w8sXt02lEAwIxIIaykFHSogIbIwdl7ucgpNRB6FEXhrj5MSiYWsNeiH/kerLVD6M8IEjwqpku2X+38Kh4sSMaJxX3x7/w+WDYmhPUCBYAV49pXeeN+d2gwbnw0BJGBLvDR+5jay0UINbLdEAoIFFWooNHpQBCAVEeX7ohIKfLKlGhtL8OJxX2xfgrtx0oRKsi03eBR+Q0c1W/ynut6aiFUWh0cJG7IfTcXS3ovgbVUiFKlBu1b2cLfxQoDgl3R0nD4pcOQi+Qoqixiv7dTM09VeY6Oolg1uEhsmKh62noi550cfBD5AZLmJ9UoaXjDyA0oWFJgdnHl8EuHsXk8PemIz4sHANyZYyB9mMHHrN5e+Hd+HywYSCvdHxUoTLxWbaRC9AtywaEFkaxHMRcOFn4DgGEy/qQT04U9FuLWfwwT9QuPLiA2JxaxObG4n3+frUS49IqBWPdQfo2rs68a3oeZ1HYAcLamJ9AUlChXalk7mmN3s5GYVQqyBuF2AO1LF5sTi3t595CQl4Db2XQ4063sxiEYssuzIRcaPmfmMy9RlrB9cGZZptlzAeDk2/1w8+MhCNP3BT2agTVIc8FHo0Nw6+OhsOH0tbn6hRnGAuoapyQxq6QSYiHJDurVWh1L+mh1FPLKlEjOLUOlWouKWpAeFEWhQq1F/2AXWnGt7+vLKjU8z+P8Mvr/5NxyVKrp1+YS8JasPIx9E5njKtVa5OtVMM9x7EYYcrKDpx0+Hdued26OPvT1XjbfuolWSBsmvgOCXdDR0w6uNlLeMcZ+0umFFUgvbDorsJqguEJdq++zuUHICcfjjiOsJPR1QYGCmkiHRNDwZHNvL7qCIrkwmVW1386+bVLVUt8oVfKv1znd57D/vxT2Evwc/Ng+nelPfxj1A2Jej8HN/9yEMbj3vkvpl0BRFNJL0pFSlMIbQzcFVFoVrwqyNmhOpJ6T3Ak3sm6YKF4VagW0FN2PFFfQ436GbO4X7IKWjIntJiJubhxiXo/BV0NoC5fYnFiWaBzsN7gpm9cg+LDvh7CV2CI6IxpanRZ3c+8ioyQDlZpKZJZl4vn2z9cojPhpgpAUIvfdXNyZcwevd3kdSq0S8bnxSMxPRHa5oa8c7j+8Sdo3tztdyVmsbJwKv+pgbIV07fE1KNQK3Mi8gfSSdJx4SNs9MlXZ3438Dg8XPkTM6zGIeT0GyQuTEeEZgeQFyUhekMyz0KwtRKSoxspmhVrBG9/X6nWMlM3M4nFppcbiOc0RLcts8xmeCjCk4PzD8xE7N5ZHDnKTOrll6VxwB0rlqrqnqTcmmDKhjScFkDjQ75+ZMuaLvsOj7BOQCqUI9+R7q+koCpklKizcogBkwLqTsRDblKGk3BoQWFbxWkKlphK2q22xsMdCfD38a+goHVy+NB246SgdDicdxuGkw7zt7V0MK4YF5UpUqMBk0EFACFCu1CB85Ul08bbnlWxz/XAB+iYb6FS7UunIL06zPsHc8n+xkIRSrYOdnsTfGLMR/X36AwAm7JiAI0lHEPVKFLLLs0GAYBN0ueQDF4zq+v1T7yAu7yayJZegIh/gi5M/Yt0JT8QvHw6ZWIB9NzNYb1CpUMBei/72bZFYTk/2bCQOSCuhyaKJ7SbC24FvqWAtMbwPT4eqb0YkScBBT4D4uVgjJV+BVnbmPacuP8iHVkfBXiZCocoLEACPC4WglKUYGeaOAFcbpCsMJLWAcoaY8jd5nh3X6PfX08+JVX1bi4VQaXQoUqjRtY2DyTktAfZSe3Ry7wSlRolyVTlsxDbVTsAc5GIIQKvn8xR5sJXYokRZgq4eXVkfMH9Hf0R6R+JcKu2JHOltXs0lJIWsn6QxbCW2GOJHL1acf3QeJEHyBkUCUgACBKwkBEJb20Gro7D+VBIm/3gJBAE8/N8oAHpyS6VF+1a2CGlli/s5pSav5WZruaT6p+s/0e/bQjtrCgEpQAe3DnCWO7P+b2E/GPqCCW0nAADcbdwB0BPOdm6t0a1VNwA184LXEUq8sfUayvVk0fqT9wHU3KO480+dkVZi6vPbGCioKIBCrYCOMihcGUUdN6mc+ezMwd+Fvi6Hh7rjTkYx/FysLR77/w1CAQk7OclT1z4uroSAJNA/yBVbL6fCWkrfxMqVGsRmlCC0tS3b1+aUKlmyuVKtQ7fPTvCeP2X1qBq1o1IfMhvh5wSZWMDefyb9SC+y3F0+DHKxkBeetjsmHa3tZbCV8helmJBWLowXjhgrlbYfHWG3cUlrJlDT38XaRE3O+EGLjLy/vziSgJ/OGYJVxUISnb0deCGVnx6Iw5ZL/DLWoXqP+BOL+yHAtXlemwO+OgMvRzlrndXSwFXBcj1tGcuBcsFpaMlsSIWhJufWN5jA2HWX1+H9Pu+z293XuKP0/VKeVVR9gqtWlovkaG1jKFse33Y8EvIScCblDIorizFi2wgAQCf3Tujs0dns83FtOOYcnAOxQIxX/6GDlT/o8wFWDlrZEG+jRlBqlbVeOAhxCcHd3LvVVhA2Jjxt6AWw0B9Ckfm2YUHVapUVwlzDAPwPf8TTgZYkRd/vXawbfsGkIUEQBBuazpCKI7aNwOSQybCX2j+VpKuQFKKLRxecSTmDsdvH4tD9Q7z9TCbJ/zc4y53hLHfGyMCR+DnmZ3T4sYPJMZa4kIYGk29TqixtsjZwweWKAODH6z8iLjeOF6rJRXjrcPjY+8DH3oe33ThHpy5oKmWzREhCSBIoVz4jm5/hGaqEp60nssqyWFURU6798+if8WqXV6s9f+XAlRjiNwTP73qe1/koNUqIBKJmmdzLdBRiXRAq9XNEkUCH3XN6Yvahr1CiDsD2SX+ZlMOFeNjhaFw2q6ikUI4yVRkI0OU3lrzOLIFZ8dt0cxO+Hv41j6w+M/MMa2fCYOfknfj0QByySspBUFJ8OuQ1dl9KngIEDO0lCAI5eoUW15uyvsBMwkmCH3ojEZJQabVsaiv3+z+SRE+0TyTTBMFz7V7A9RvA+E6tLJbYe9l54fTM05hzcA4ySzOhImlyWksUQUi5IbO4DH4udnhcZFA5kyTBXosCkibwwlrb4aOxv+FR+U3IRXIM8R9i8lrWEsOgX1yL0BNGFedvZuIe4GKNzOJKaHQUegU4I//2c+ji0Rn30mkylSEWuDc/AhLM6u2D//Tzx0sbo5GUU4aefk64lJxv0jZGjZ1TWslTZrc0MF6B+RX5NQqK6OXvhJ2vzEChNgRKrQJdPboiqSDJZKL6bq93Ed46HDZiG3ZCUVt42Hjg3MvnkF2ejVY2rXglsAA90GFK6zpwCFWuNapKq4NGR7G/le4+fDK9rbsNgtysodKqQBIkz74HMPhFd2/VvU7vwRhRr0Th2uNrrBqrsKIQr//7OlKLaWJKSApx6u1+yC1VwsuRvjZv/ecWu4hkDowXPAUlSzRz8fULNZvAZJVlYXTQaPyb+C9v+7jgcdDoNCAJssHuK4w9Bvc6YhY+uL9RczYaVz4cxJKGAPCffv7oF+RiUu1QX6jUVIIA0Sil+PUNdzsp/n6jJ3QUhYJyFdxspQjxsMXWy6kIbUX/vgrKaQXwwLZucJAb+maGyGP21wUM2c0slHYz+j2WKTWQi4UoV/KvYwFJoH0rW2x+JRwzf6MrkPa92RvphQqIhSRspSLkldHK/oN3DGSNRkeZ2H4IOeSxj5Mc8ZklEJCEyXEM2Wx8T/r3Nl9dLyAJ2EiFKFdqQFEUCIIwIZq5SMkrb7Zkc0G5ivf96nQUCAIthvjp5G2PDH2gJLfNjBJKS9CqsA0jNjRKexaEL8D6K+t5QbwAXY3XUGRzqYpeUP1lzC/o6dkTbtZuuDDrAhRqBYb4D0FSQRKuZ17HwyJDQKiN2HzVDECH+c7rPg/fXqXDyxlvZwD4484fTUs2a5Swk9aun496JcqstVdT4t3e7+Lf+//idvZt6CgdSIJkVeN3cu4gVEqgmKTnFTIdHYT4NFXucO/xaSVpjVJ50FRY1m8ZzqScYedjXLzZ/U0zZ/z/wcjAkRgRMMJE4AUA/Xwax87NGEzfyPSrTQ1mMfGLwV/gRtYN/BX7Fy48MtgCigVizA+fjzWX1gBoWLsgESliveSrAkVRKFGW1J1sNlI2EwQBK4mwxZHNzY+Ve4anHky5FOPTxZDNfdv0rdGE3sXKBZPbTwYAHEg8AADYG78X0pVS9Py1p8nx9qvt8er+6knshkKHT45iVwyttCNhBa2GVq2pdBXwdQWspVp42XmySj4u/FxohQgBAQhKgmLRDih0mVCSCfRz1DIgkQ3a0E9GuGR1ZBtTFeakkEmwpSJhpe0Hua4H7GSGDtPFRgLoyWYm8OziA4P6rj47wysPDWXO4zvzTfYZZTNJkGjv0t5s4vGyM8sAAP52tGq5ezU+w/19+sPL1ov3XCoiCY9kExDwvQMeFDwweX/ro9cDAKQC+jsb16kVwtsEYFLIJIwMHGlC5gGGElegdt5XDGlgHAoF0OE7GUUVoCjA014GElZIexwGEdUKgOF74U6ySEqG90e0g5utFBP0n+/EroaSawFn8sqQl2otxQ8famEQC8S48OgCtt7eWuWEkwFJEugT4IoxwSMxKWQSfB18McR/iEkAoJ3UDmODx2KA7wDWq7wuiGwTiUkhk9DLq5fJPpVWhTWX1tALT0ZkyD+3HuPE3WwE/5deaLHSl9fTv1cDJnfzQkxmDCSfSSBaITKZACjUCoS6htYb2RLkFIQXw17EpJBJmBQyie3DmRA+ESmCn4s1evg5oZU9TSJ3cOtQpa+agBRALJCAgvlFN3O/D2PoKB3UOjW6enQ12VepqYTj547o+GPtU6SNQXxKYNnpZSbbmT6mlU0r3vZgp2Ao1Ao2v2DVhVUm57raSNHa3rBQIiCJBiOaN0RvgGylDLKVshaTlWCMcF9HRPg5YWSYB7q2cYBMLIC1RIhKtQ6H7mQi8guaTApwtYa9zKAUZuxY3vwzxuQ5fd6jbcFmb7mGvl+cNtnPICGTnigx/byAJNDRy+BFznghlyn5aplKtRYEQaBfkKECqY2THMNDPTCwrRu6+ThieKgH714CAN0+OwH/D/jqMRHHaoH5bQhJgiWVGbX11ss0YWwcIGoMsT5IUUfVzJf5tS3X0P7jI9Ue1xzw3A8XMXvL9aZuRo1hw7kXa8yEOFKgr6sAx4BGac9Q/6EAgD6/9+Ft91pnaudUX2DIiOfbP4/2rrQ1TG/v3uxCP7Nw2vknw8KeuTwABgRBYGzwWPYx1yP0UfEjZJZatjZqSKy9tBbRGdHsvaGmsJfaI8gpqIFaVTfYS+3xUthLAOj7rVanhec6w9gzlhiFtIooSHXtQOj1ccZjmZYM7sJLUWVRna1RWgIY9bLxvDXQMdCiVdr/F4gEIrzc6WWz+4zHho0F5jsx9sJvChCfEmyGSm/v3nil8ysADFZoAF093MWjC/u4JvO6uoIkSGSXZyMhL6HK4+YfprN86np9M/0B9zdjLaGtLFsSnpHNz9DoyFXQCi1mkl3XIConmROremN82BivTS6KlcX47eZvdW7vk6KkUoM7j7NAUHQwnbTUECSWWZpZZYlFZ2/DQNhZtQR26hcAADqiCAQlrLWNBvNZMz563PNJgsSe5/fgzMwzvHOMfWAZfDY+FIS+C2EIKW5wXH49pqX+deUR+/+KcfwyUImQhFIfmiQXyXlq97bObXnHusl9AFQ/iTb3XIzCmQKFlKIUVmm9aRY9gREJRBCSQqwYE4kPR7ZjSduqYFNHZTNzezX3Pl7tYygRspWJIBaSPO/arGJ6waGtc1v0cf4Q9upZ8JWNY1//9b5+2DC1M6/9A9oafJm5BHNLJpu56sxfxvzShC2pO8yplOIzS1j7EwCQ678jkYDEAI7XoUwkQFxuHPv4TjY/DOxJSr9qAua5GT+4upb2WonkcLDmKzNf7uWDv98wXXg0B2YQx1UUbZ2wFc5yZ2SUZqBUVWriXV9bMKqE5eeWm+yzdP9j+h9mAZbxc2wq3M29C5lQBgoU7uffb9K21CekIgEq1Frsu5HBbpOJBIjwc8Ss3j7Y9loPniLYHDRaHY7fzcajAsu+xNl6v//2rQxVClz1tIFsNoTOAkBRhWmpprkQxb5BLlg2JgTjO1memHLfB/fe0cpehl9ndsNvL/OrGJgAWwbFnLYEulpj3sBA9h5QVsPJj7kKhOaIm2lFOBHfsB7D9QmuOt04dJKGGgQIs4veDYFBfoPgIjfcb7ivS1HmfcefFPmKfAhJoUWSoW+bvvh2xLdYPWg1Xgp7CWuHrkVr26rHaYP9BmPHpB1mScCUopT6aHatYS5krSWDufeVq8pRUFGArLIsk2MoaDE13BubZnWHXNxyx53G6OTeCTM6zgBAV3u1xKqhmsJOaoftE7dj9aDV+GHUD+z2nl41G6s97RgVOArfDP+Gfexm5YYdk3Y0WCVIdWAqKo298JsSg3wHIbx1OC8bp0frHnirx1tYMWAFa2cpIkXwsmu4hc1h/sMAAPG58VUex3BTszrNqtPrMIuh3Hwu62fK5mdoDJQqSxH2QxiCvw1u8MCN+kZ6STpr5ZBdno3uv3TH7AOzAZgnm+9llWLw2rP49tR9RH5xCrfTDT+47q27Iy43Dv7r/dmE6UpNJVqtaYXADYF4VPzI5PmaArmiL1Ei2gOKoEkNMeULFyWtcCtTleF65nWLpI4Hx5NXrusBW81z7GMKBFZHra6xbxAANlG7WFkMjzUe+OriV7z9E9pN4JXsPMgtQ76F0mF/F2uAoiesOkqHSrUWP519wO5nwrpqAoqi0Hn5Mfi8dxA+7x1EbIYhkODTA3HYqycCQjxseRYaAJCSr8A/tx5DqdFCLpLjZPJJeKzxgMcaDyTmJ/KUoRP1/pg1IZutxFaIyzGQceVCg2pNoVagTKlBa3sZ+usD8hRqBUYEjEBoazvM7usHpxr4ynHVaOJaKJsZpbFMZHoOY0EA0Oo5fxdrlowHwK6IEgSBMPsJsNNMxNBgg92DSEBiTMdWPA/IMI5akkswG4dStSQwyfPDA4ZjgO+AJm5N3TBg8wCsvbSWfexsLUFhuYp3fVtxJmZ9OerIvMpkzNw3k328+NhiRGyMgFanxcaYjTiSdKRByWaxQAwhKWS9iOuq6JEIJagUXOVt+2Rse4RXU73AgCGbua8/rcM0tHVuyyOZ60qQxOXEwX2NZb+7qsjmo0lHWQumhLwEtl/zX++PpIKkOrWnrlBoFKw3eW3tm5ozSIJezDx21zCWkopICAUklo1pj94Bzry+0Bze2GpQwG48n4znf7rEu16O383Goh30vbeNEycIktN/qnV0H30yPhsSIYmvJtNqejWHOGTuEeaqYORiIWb19kWvAGeTfQx4ZLP+tZlmDmrnBm9H/jWoNbrmuW15pY8vAlytWTX0uztvsyrv6uDz3kHecxljx9VH8HnvIH698NDiMc/AB9fD25yVCQUNxAJxo9mCSIVSLIpYxD5e0msJ+7/HGg94r/PG2ZSzT/Qah+4fQqs1rbD6wmoAwJ6EPXCQOlh8jyKBCG+Gv4mlfZbij+f+wKKei8wexwVBEHi+/fNm1YXG5eVJBUnw/cYXy8+aLirWF3bf3c32v8b2Wi0VzL2v/fftEfK9JesxAk5WYna8/bSAJEjMD6cFSNnl2U+1jQYATAmdgqV9luI/3f6Djm70PW5kwMgmblXzgJXYCgt6LGCzXlYNWoXn2z/fZO1h+heufVBjo1RZCr9vDLktH0Z+CCEp5I3XP+n/CdYNXwd/R3/WivTdXu82aLvCW9OWPuZsT7jIU+RhcsjkOle5WomsIBFIeJktVhIBjsZl43JyfhVnNi88PcuD/4+QVJDEToLjcuOeqFS7scGsAi3tvRSfR32Oa4+vsfvMERsnE7KRlFOGr47Rvm8304rQwdOePT5PkWcSnMSkTMfnxvOCtRg/sMaETkdBITQdUJN6D+bHpY8BAPYS86V83EluZ297rH0+En7f0Y8lOn8oBQkorCyEq1XNBmDM6lgvr164n38f/96nPUqN/bIOTD0AB6kD7mXRg+nX+/qhsxe/jQKSAAmaDCcpKyTllPE8Y1ceisfsvpbDvbhQaXUo5Kiib6UXseXgv0elsNttZZa7rNxSJZb2Xop9Cft424cHDMedFk7oAAAgAElEQVRzaTRJTxLArN4+6OnvVG2b3uz+JqxF1oh5HIdrWVG8fQq1AuVKDS9ksC5KUKGAxHNdWqNSrUWgW81Xr2dH+sHRSoyh7asObRgZ6mEyYeeuiDLKapkF0njFuPYoqdTA0cpwY7fnqPG4iueWhvnh82EvsccLoS80dVNqjekdpmPr7a14VPwIbx97G21A/46drMQoKFehfSv6tzM13Bu9AwzXOrc/sbJ+bPK80RnRKFYWs4EbXIKgIbBu2Drcyb4DPwe/Oqs3rERWEBACLBgWDKVGB0d57RTSTHWHRCjBkZeOsIPYpb2X8vzglFolu0BRG9zKvoWCigKL+wsraS9VbhgVALzf5338c+8fiAQiDPEbgsNJh0FRFPIq8rAnfg/u5t5ttJJ4gO7frERWEJGiWlfUNGeY82Fmfj8MQjxs8XpfP0Q/LEBXbwd093FAdkkl/r2diWuphTiZYKgw+OwgPcZRanSsAnnBXzcA0FU49pwgPxVnEZBRNpME7YPMLBhx76mHFkbiZlrVeQiCKshEro3G8FB3pBUoMLmbQf3jaiNBgKs1knJoQYCxl/PAtq44HJuFmT3bYGSoBwAgMpBegDibaOop/npfP/ysDxT8dWY3vLrZMN7LKVXyLGC4WLqbrrJY8e9dXqVOY6GhlLcNCXPk/T592OHP07vifxd3Iia/cYmsF0JfQHpJOmQiGd6KeAuJBYm4/vg6+vv0x+83f8e1x9eeyI/0cvplZJZl4uiDo3ivz3ugKKrWPsY1xaqBq3Am5QxkIhmc5c746PRHJuXlsTmxSClKwbdXvsXH/T5ukHZM2TWF/f+jvh81yGs0NkYGjsT88PnsfaVMXYaLaRfp6p4yL2h1FNQl/XnBl08TOrgZQuEa6rppjlg7bC3+ufeP2Tyb/89YNWgVdsbtxOig0U3aDmZ8yVieNgW4HvsECER4Rpgcww1in95hOrLKsngLnQ0BJjyREYNYQp4iz8RqsTYgCIIXrg4Ar0X6Ye62GFxPLUSEX/VcRnPAM7K5CRCXE4f5h+cj1DUU60esr/X5UWkG4utMyhkM9B1Yn817YmyM2QgCBC/sb8XZFchT5OF48nEAwMudXsavN37l/YDMTeSNx/zciWF1xJ5CreBNGoZuHYotE7Y0qv9R7ONis9sJih70v3ucXn3r79O/2uea0bMNfJ0NSgZr7XAoBQl02Uj11qQADCq6f6f+iym7prArltySFADsTW73dTplflqPNvB24n/eQpJggwvl2t44lZADLUWH6jAf+530YiTllqJMqcX0iDagKAqf/BOHiV092UUDAFAYhSIV6C04lBr+dsYb2hzKlVqMCByBEYEjeNsZT1gA8HGywrIx7S0+Bxe9vHqhl1cvJBcmw3+9PwBASnigksrEC7tfgGfFNoS38QEALDm+BIn5iejtVfsk+7XPd6r1OWGedgjzrH5S5e0kh7O1BLmlBnJIwwkUc9CTyBIzCmkAmN7Tx2SbA4cs8XdpnmFPNUEf7z7o492n+gObIWZ1moWtt7eabHewEqGgXIVvT9M2B6sm8D2XuXYqQoH5QVKXn7ogtTgV7ZzbmfyW6hvzwuc98XMMDxiO765+h9civaosQV0fvR7u1u48pUhGSQam7Z0GgLbRGBYwjN03Omg0poZOxV+xfwEAvrr4Ff7b97+1bp85D/nssmy4r6Hb8nfc3wBgMiAdFTQKo4JGsY/HtR0HgLaz2BO/p0bhJPWFn679hF13d6GLRxeIBeJaZwU0Z5AkARiRqtzFNYC2rfhgZDuTc60kQlxLLTT7vDuvpbH9J+NnPKMn33+8rbstTsTTRDVDFhaUq9A30IUlqrnccYCrdbUBe1UphrnKZk8HOT41sqQiCAJvDvBnVdgM2fwoX4G+X57Wt9mGd56jlRit7WVsOB37WiSBD0a2Y8nmgUYLk7fTikzIZq2Owkf7n8yypj6QU1qJPNEayLU9odQMR06JEl+fuI9Vz4VCImye1TwaLQWxgIRKq0M5eQEVgmicSIvHvOP7EJUWBXdr90ZXTfo6+OK7Ud+xj3dO3km3VafB7zd/xzvH38Hbvd6u8/MzfevVjKvo81sfpBSl4I2ubzxZoy1gathUTA2bCgBILUrFR6c/4pWX55TnYMKOCQBQI6sSHaXDjL0zMClkEsa3HV+jNijUCmgpui8JdgpmfalbOtyt3S3OhWf8dgUZhQo80JVbFEW0dHBVmk2pZG1sDPQd2Oz4i+aA5jI3IQkS7Zzb4cfrP+L7Ud83eljulYwrWHTUQBp/P+p7s2HuVmIDASISiPBBZMPbDBEEgfYu7au0GEnIS3hishkAnORO+O3mb/h5zM8QkAKMDPOAXCzA1kupOJeYi49Gh+DQnUxetlX7VrYm47va4NcLD3H+fi6+eaEz7GR1szjk4ulcJmzmOJF8AqdTTmPDlQ3QUZYnBpbA9bOqSjHVVJh9YDZeO/Aab9vHZz7G+ivrkVyYjBfDXoS/gz/e6vEWe5N9q8dbNerIFBy/P8YzB6C9jvZO2cs/Vq3grTqdfHgSNzJv1Ok91RXXUw3fj6NqDvu/iPLG+ODn4GPvg9FBo03IXi6+nESveo/QK4kWdP8Q3eXfskSvOTLDEhgPYrlIjlmdZqFfm34YHTQaPT3Ne2Yp9JNkqdi0qyBJAlJtJ8g1fWCjGY+MwgqoNDrM5BCUP59PxqIdt/DRPnoCWaRQY/OlVEzbGM1vl4rvP1Smf5xeyJ/AfjGpA4zBeFQaBysxsJXYwlozHA6qN1AXvZKXrRcG+Q4CALSXvQ1HEb3aqyKTWP/NDVfohPfJIZPr8AoNi0/G0GWJQ0Pc8GofX2x+JZzdN7S9G/oHu2BoSM2rI1rZy/Bcl9aYN6DxVJXPwEdnj868x9++2BmfTwyDk5UEKfnlUGspeDrITPpUrrKZ6Qs2jdvEOya1mC7Bbi4J1NWB8edk2m0JC48s5KnCAFo1cSblDCK9I80q7D6I/AChrvSAjQmjrS24/bOHNd2HrzpPh/0xRHOP1j1Yi4rqwCzKNibZvPnWZgDAa51fg0QoeapsNJaNCYFESKKLtz2GtXerVb9Wlarko/1xnONoS5cXe/DJ5tmRfnDW2y0xNgjlKg2sJEKEtLLFsPZuWF7LCcPD/HKL+2piHyUWGI7R6VeN/8shgKd0N/VBdLU1JTHXPE+XSM+O9MXa5zuCIAgsGhyETvoKqdwy02sovVCBP6MN9mdMQHJj425WFsqFp5ErWYVH+Qp8eiAOu2PSceF+XvUnNxE0Oh1s9ZPCPPGXKBeexpcXv2TFKRKBBHO6zanqKRoN9eXfzPhzRnhGQCqUor9Pf0wKmfTE7asO5oKz/nf+f+z/NckfyCrLwrY72zBj74wavy7XH3RZP9Ow2acRIpJAaSU9HzDnVf+04H+D/oe1Q9dWf+AzPEMjgqma5noGNxYOJh7EzaybAIDeXr0xwIdvd7hl/BZsGb+l0dvFwEZiU2V44umH9AK9JX6lpvCypcdcXO7vld6+8HOxQvTDApxNzMUfl1ORWVwJiYhEVkkltkU/mY3sin/v4sy9XCTl1M888JmyuQnAVfO+vO9l/Lfvf2uVEJynyIOL3AWOMkccun8IM/fNhI+dDz7p/0mjrzxVhej0aPxw7QceoR7gGIBtz20DAHzY90N82PdDi+ffzy7Fl0fv8bZxy06nhk7F9L3TAdABXx42Hrxjp+2dhkNJdBo7U3be2JPk8+k0QWGvfhk2WoNKjYQMf0/eadZ70RiTu3nxSl2/GfkZivqrEPTZSgDA0hNL4SR3woS2E6pVSDBl4WKBmFVqvLb5Gr49XojPJxle4+KDPBy/m408vcLY0iRVADu4qN8DAByOzYRGR/FsFg7cMpTqbzyfzKqbSir55LLCKDSoUv+YsfEAgHGdWvH8iBlM79kG+24+ZoOVuIjNKMamiylwUtddQSkSiHBixgm6Dd9eQLnCAQV4FSXCvbhachPEp/Qix6qBqxpcCVoX9PBzQsrqUWb3dfF2wKZZ4Wb3WYKAJOqkxn6G+oO91B4jAkawfmEjw9xBEiTuZBSzv9l3hgabnMeU2OuIYrx15C0AwMSQiVh6Yimyy/n+/41JZj4JGO+02iy6AbRS+ePTdMnq96O+N3sPDnUNxZ05dzBhxwQ8KHhgsr8mYNo1MnAkLqZdxMx9M7HlFn+AvGn8phpbPDUW2UxRFD46/RE6u3dGniIPL4S+gDnd5+DTs5/iu6vfQUfpsLDHQgQ7m15nLQkv9WiDl4xI4JrCy1GON/r64Se9etcYZxNz0S/IBQ5yMYLcrOHrzCdP7eQirH2+I2b8dgUavSJZrdFBLCRhJxPhp+ndat2mEn2In5+LFZJz+cSzsAbjDW5QLVMFw80TmNXb1NZiandv3HhkmIy+3MsH4zrRwWsfjjJ4sC4cHIjXIn3RftlRVJgJCoxO5osn2ro3XJp8Vdh53WA9NXdbDFxsaDL9XnYpBrVrnrZ1ai0FFxsJ8sqUAEF/ttwA2Zc7vYxP+n/SRK2zjC23tuBB4QOkFqdidpfZNVL0aXQaLDm+BD9e/xFetl7s+KyxwCxwbr61Gd9f+x75inw26BYABIQAF9Mu4peYXzC321x0b93d5DmYe0CpqhQz983EyICRmBI6xeQ4Lq5kXAEARL0SxcsieZohFBDI0Vfm1WSxrKXivT7vNXUTnuEZTDCj4wycTT2LEmUJHGQO1Z9Qjzj64CgcpA4oWGpeVDm94/RGbY8xbMQ2OJ58HJfSLvFCLtNL0rH87HJcz6SzPAb7DX6i13ml8ys4eP8gjj04hlmd6aDBd4bR4+6Qj49g/80MlFRq8GofPywcHIgNJ+9jzfFEqLW6GnFMVYFbCf0keKZsbgIwZHOQUxC23t6K7bHba3V+QUUBHGQOGBc8DhQoHLh3AMvPLecNdpoDVpxbga23t7L+nwAwLnhcjc///EiCyTZuyraAFLBJnYwqbEmvJXivt+GmHfUoCoGOgaynT2N7TZ7JoIl1ia6tyT5hNaFDVUEsJCGmfNDKKghxuXHYGbcTn0d9Xu15DNnOLEqoNDqciM/GjmtpPH/GJbtu4/eoFMToS4StapD+zBDIIgGJSV09TfZ/djAehQrz5dfGyapM2fHcbTGGbRZS7JnAQHPprLuup2OX3goEAJ7rUnXyeHWoUGshoOhrrVJwE0eSDWr6yDaRlk5rdLw1OBCzevs0dTOeoQGh0Rmu99QiWtXbJ8AF3o5yBLhao4MZmxVS3+dohLFQ69QIcAyAXCTHZwM/g43YBu7W7vh88OcIcQlpcM+z+gJjp1Qbslmr02LJ8SWo0NCVE9WVl8tF8lqT2QxKlCUQkkKMDhwNe6k9zqWeM/v8NUVjkc25ilysPL8Sk3ZOosccek9pRrX3w7Uf8EvMLw3ahpaAcF9HeDvK4eNk+h0yfvkqPYFsDkxpeLn+/qbSWj62Jngt0g/BbjZsRRRAk8WTzdyTzUHEsdpgAgKZNloie+QS/vaqxjaMOpG5x3NxI62Q14b6muhUB52Rjco/tw1VEok5pWyFVaGFsOTmAI1OB7lYAAqGMbKfgx+ripIJzftjNxUCHQMB0POEFedWYMutLTXuTxLyErDu8joAqNI6qaEgEogwOmg08hR5SMxPRH5FPu9+bC22xq8xv2LTzU3YGLPR7HMce3CM/f+P23/UaPzOKNvaOZta+jytkHPmHs42T3d43jM8Q3MDs7DWFJWOZaqyZiWgNAbzmTCiRwZHko7gl5hfkFOeg3HB42pU6VIVunh0AUBnFBhjeKg7FCotfJzk6KGvoGPGa+bGWLWFcW5HXfGMbG4C7Ly7E22d2+LevHuwk9jhSNKRWqUyl6pKYSuxxedDPsfDhQ+xYsAKAHhiH8XYnFhcSrv0RM/BxcH7B9HGrg0eLnwIahkFahmFlYNW1vj8IoWpLUJ0cj7iMw1lC4VLC0Eto9iyvM+HfI7/DTaUsz1c+BCJ8xPZlSWlVolbWbcQnc63cWgoKLWVsNJ1hVRnWgr7JJ2oWEBCSLmis2Qj3gg6jM6uQ5BUkIS3Dn0I/3VdcPqhKaHxsPAhTj08hbHBY9ltXH/HSrUWmcUV2HDyPju5yiiqQLiPI0tS1QQSIYmvJnc0u+88pwz1j8upyCmlCRNG2bzj9Qj4OVvhZloRLj3gJ61a6jgZInzfjQxcS+GvgJZxCOjuPg51VrAxGNDWFSqNEPZiD5N9zcFji8Fbg4Nq7E39DC0TDFEK0EGjM/bOQHTeTziyqAdOLO4HPxdrbLu9DacenmKPYxa5SQF9rzg67ShIgsRrXV5DyfslyHw7E0t6L0Hc3LhG8T2rDzB+bV9d/MpiSfatrFvs/28ceAPjttMLtQyqIyysRFbIKc/B8rPLkZBnughaFRjPtjnd5+Dhwod4uPChST6BlajmdgHMuQuPLMSXUV/WmQQ3h3t597Du0jqotCrewmx+RT6bTL44YjG7vS6Bu4f/j73zDo+qSv/4507NTHovpNNr6BBQOlLsYsHFgroW1J8FFV17d+2ryKq79hU7AiIKSpcqvQVCKCEkJCG9Z+r9/XFzb2Yy6U3Q+3keHmZuOffMZObOOe/5vt837WdOlZxqe2fPEib2DmfDvPGse6g2xfO2MYmM6xmqBCetDqebOtgV2QO/uNJKQbkFWxsDrF1DfVh5/xiGxAUpyuDrRsbxagO/yXVxDXTLQdj9mcUe+xqjMQW1ViOg1QisS/UsKFhYYaVnuC9pL0ynXxe/dpvoNIXD5b7hcIqI1I49S3RfcLxQWrD+YOMJpXhiazmWV84Hvx13y9JrKRvT8tl01N3SY9PRAgrKLbw9SxrjBFpv5dg9xxRbieb4CHcmR/7vCBd2v5BjRbUZI829l7ke53o/6kyWXbuMw3d7/haMix+HQ3RgdUrf/fyqfHae3skvx37hxd9eZOnhpYD7fC3KN8rt97whbE4bOo2u0xWGfySTXDIJkppRp0RFRaX9kMd9jdlFdBT5lflc2bvjbZFaizxGdv0Ng1ohyO7bd7Nk5pI2Xyc+IJ4+oX34NuVbj31vXD2QjQ9PYN1D4xVbN3lBf8GaoxzJbdsigV0NNp+bFFcXU1BVoBQ6GxE9gi2ZW3jgl+YXySizlCk3AKidKLdVtdv/3f6M+qh9U7Pa4vNTXzGIY3kVTHvrt3qOducf5/2DMO8wJaAre0NbHVYGvj+QkR96VjTtCCyOKrx0zVetNRetRkAQ4EBWKe+tP8ah9DDyK/N5a/uLHC/dzYTPPP1H393xLgDDompT+lwnPFU2B99sz+T1X4+4nVefQrIxGisa55om+/iSA9zyiVSdXlYlext1lFTZKK+288yyg27n3lhPsToAPy9p1fCXlFyufM99scRV7Tyqa9tM+gF8DDqsdqdS4FHmr1TUQ+XswHUQ9tLGl/jfvv/x9Pqn+fWYVITVYrdw3eLrmPjZROU4uUCgViNNdFuiqD1bSQiQ0voXH15MWmFavcc8u+FZ5fF/dv2H5WnL3YryuD6ujwHhAyizlvHUuqd4ZdMrLepfXmWeR4GQut6Mrr/nTeGqwp63ap7bYkJbuWfFPcz9ZS6bMjZ5WE7JCpeBEbUWOq5qvuYgiiLTv5jOiA9GtL2zZzG9InwJMhuUgsZWe8PpjHIxwsIKK2+vlj6/m+sstLaWwzU2VC2ZcMQEmpXAuJxJVlxjzfHwVM8MLYChcUFuzwPNjSt5HE6R43meQduiChuB3tK5Wo2m3SY6TeF0CTaXVdsQqf1cl+i/pEInfcdEESa90XxhSH08tng/zy8/xJ5TrR8bX/fhNma51L2QF9nSCyo5VrwPAA3SPWV0zGi0gpb+4f3b0OuOYXeOew2V1gSbB4R71vHoLMx6M71C3L8TwaZgbA4bNof0nSmsKmTof4cy5fMpPLbmMaVugOv9dXDk4Ga9dpvDhl7T9mJN5xJD4moD6/JYX0VFpXOQ/ekbK4TXEYii2C7F9TqSWf1nASgZ9jJysLmuqKQtGLQGiqqL3KyxGqJbmA96rcD7G47z3vrW2f/JqMrmc5SMEsm0W05RXjFrBdf0vYZya+3Ae3/ufj7f9zmLUhbVq1YutZQqEz+onXx2lh/xyqMr2Ze7r959csFCeUIqF6FqDW0pBvHixBc5POeUMsmSA/JysRSAZanLWHF0RYNtpOSl8Pm+z9mfu79VfciryKPAchSD1vOG8/ujE+s5o/kIgoCriM/PcRmHbishRntjg+dU2aoI8Arg8TGPK9tcbUmW7jnN0XomgI9f1Mdjm8ySu0a7Pe8T6cf4Gl/m9H9eyImXphPiUxvIOZFfwbietYWwskuqcThFNqRJKiezQcvkPuHYnKIyUZbbmtRAETtfL3fFjhxg3nKsgJ8P5DAg2p8TL03n/snN90VviFrLDumzOTJ6JM4nnXw1o2VWOCoqbeXekfey6nrJq3J52nJl+49HfuTzfZ8rfmGuZJalUaFdRwnSffDPEGzu4teFFbOk+/jCfQv5fN/nHv9S8lIYGzcW55MumRyP1dpQNGWjcffwu3E+6WRQxCC39OemsDqsLD281GPAPGfYHMSnRJxPOnE+6WxRmp1W4/676Dp2aCk55Tl8e/BbqmySqk4eyP589GePxWt50jE+YTzOJ52EmkMVz/DGyCrNUv4OH+7+EIDs8uxW9/lsRv7Nu2JwNIHeBrKKq7DYHRzLK29QFSxX+l55MIf0Aing1BbVa32ILSiNGxNkJvX5qVzYP5J9mSWUW+zotRquGRrD30bE1ntOhL8X6f+8EDkBakYTlh03JMe5ZUvll1v4bEs6v6cXKsF3nUboNGWz0+XtrrA6FGXzvyZ9DmgQ8VSdni6ucisA3VxOFUptHcsrZ/PRfPZlNj/ovCO9sN73xHUcV16T2jswXBIVzOgzA9sTNi7oekGL+9rRyAX+ZJoTcF2Xvo5vD0oKr623bGV07OgmzuhYUu5M4ckxkvf/8C7DMelNpBWmcaRAEm3I91YZuYaNxW7h8l6XIz4lEu0bTUZJBqn57jVq6mJz2tqckn2uEeFfO39qSYaliopK25GFEA3FfDqK/Mp8HKLjrA42PzDqAWb2m0lxdTG55bU1bzoi2Pzw6IcB+GTPJ+w87Tm3c2VkYjBHnp9GkLeB73dlsXh3pvLvp/3ZLRpftteC/9mVV/UXYND7g4Da1F9BEDz8IK/57hoO5UtVh5dcs4RLe7n7HJdZy5SJH7irdtsDp+hsMD32RNEJpi6cikFrwPK4Z3BbLnoxKWESe3L2MDbOU2HbXLRt9Oq5aP5GsoqrSP/nhfgafPE1+LoVZrrkK8lO4vg9x0kI9Cx6c/3i69mVvYu+oX05cOcBj/1N8fRayTLEIIQSE2SiqMKm2Dq0xZOxIYoqbZicDas8LA6Lx83P9abz3I8pHufULWhUF1d/R0BJ45CpaxWSU1rN1H4R9AyvJjW3jKgAL95bf4zPt0qLMN5GHSaDlryy5i+caDQCccFmTtZM1L/bmcnlg7tw7X8lf6N9mSXt5vvkUxPYlnybjxHtF31We0qp/LmJ9vMM6nyw+wM+2P0B4d6eizNv77mHfMN+5NjTnyHYDFLhWQHBTcFcl/NiznP7rgqCwE0Db+LjPR8rv8eNIQgCIiJZZVnklucS7tN0obDlR5YjIhJsCq53f3vcO9piozF35Vy+PPAln172KTck3aD4Mi/YvoBr+13rdqyr+loQBKwOK4fzD3Oq5BQx/jE0xCOrH+HzfZ+3uo/nGvLfNLhmkXXFgRzyy6311hSA2iylrccLGdVV+pxcOjCqXfoSHWgis6iKiwe0rD1BEBS7iDd/PYLTKaLTNv1ZvWl0Ah9uPEGAqfGAmE6jcfNjfuPXI3xRUz09Nkj6Lmo1AnZn+wbdG8LVRqPCYgdBCjaH+/qiFYw48RyP/P3THaRkl3L0hWnNKrwoI6uo//F9rYihoQK+rvyWlsf1H/7O4xd6+vVWW2vfJ51O6uvlSd2UbWfrGOXvg//O61teByDUHNrkvczmsDH+U8muRiNoiPJtn+9JWxAEQXl/d5zewbi4cUCtarvua7I5bTicDqwOqzJ3k++fNy29ic23bG7wWn9FZTNA3yg/LO28AKeiotI0kT6SZeTS1KU8NPqhTrvu5lPSfdDH0HCm9NlAjyBJxPb0uqd59yIpe7zaXo1W0LardZVc4+DhVQ8T4RNB9gONCzYEQVAy6+7/eq/bvveuG8LUfhHNuq6jncZgarC5kxBFkYN5B5VVbddgrllv5lTpKU6VnCLcJ5xD+YeYkDCBNSfWuNlQVFgr2Je7j+LqYvwM9dtoVNmq2Ju7F1EU0Wv1DIoY5KGEaopNGZs4L9Z9Yp5RkkFWaRYbMzYC7oHtzNJMThSdIMgUpCjsXp78MnOT5xJkck+vbAkn8itIigmgT6QfX/6eQXJiMFuONz+9NKu4VlFg1BlJvy+dwqpC7vrpLjd12s9Hf2Z0zGiSImo9DUVR5MAZKcDcGmP84upilh5ZgiCa+OekF7l4QBecokj/p6XrdkSw+XBOKZbKnpj1Y6jW7mN71nYGRQ5SbngWh8VDwSf7Yj80pSe/puSy51Qxkf5e/Ph/5+FwikpwtSl6Rfjy2S3DCfH2VAi6Low9e2lfZo2Iw2J30OfJlQyODeSYi5ra26hzK0L00eyhnNctlKZYfs/5FJZbGfPqWjYfy++wqtU+NcrmUOvD/OOiEG5OHt4h11FRaQ49Q3oypesUVh5bycx+M5k/bT7F1cU8vuZxvj74tXKc7MVfUHVa2TZv1LyzzseztXQN6kr2A9mN3qvjA+I9tv334v/yxpQ3mrTRkLln+D3c/MPNlFhKmgw2O0Wn8ns4f9r8ZrXfGg6eOdjidMMjBUcoqCxgW5aUip9dls3WzK1KYKTSVsmO0zvcznHNpgJ4a+pbzF46m21Z2xoMNpdby1lxdAVDIofw1ZVS9seDvzzI0npVGNsAACAASURBVNSl5JTnEOHTvAHvucis4XG8siKVrTVjlsl9Gn6t/7yiPw8v2k9WcRUxQSZuH5PYLn1Y/9B4yi12/Jr5O14fX28/hUagWVXNH5vem4em9Gwy+KrXCljsDs6UVhPm58UxFx/kh2qqrOs0QrsrvOvD4RTdfJhLq2ptNMJ8fdAJRuya027nOJ0iKTW1Q/LLrW7qS1cKKgvIKc/BRxuPRhDwN+spKPcUhTSnarzscZ1R6BmQrbRJ/X3usn5k2aUg9k3JZ38RuVcmv8I/zvsHNqeNu366i/25+8mryFMKftdFLq4OkP1ANmHeYZ3V1UaR63UkRyfz4sQXWXFshaIE3H/GMzPyaOFRaTxeM3d7ePTDfJfyHUXVRY1e56+obAZYNGcUDZRjUFFR6UCCzcGMjhndLE/59qLCWqEILs+mWkj18dS4p1iwfQEp+SlsObUFnUZHubW8XVXNAEOihnB67mle2vgS83+fT4W1olkiGYB1D44DJDu0yxZsYndGEaEuxVY1AvSN8q83LqUqm88x9ubuVVTNgJvSSZ4kDnp/EHOTpWIXA8MHsubEGrdV8ftW3McHu6XKxq4TXVcbjUdXP8q/tv1L2ffJpZ9w48CGrRXqY8wnY1h53Uol7U4URZLeS2rQfznmTfeJZqx/LBpBQ6SvZxG15uJ0iqTmlnFetxBkGV6vSF8l2FxtczTbZsPhFNFqBIJMQQSZghjZZaRbsPmun+4CIOO+DGXS/PGej5WAemu8sEd9OIqsspPoxRiiA7wV+wWZhooFtYQL+oTzS0pt6sYzyyRlsk4MxSkUM/yD4fz34v/y98F/B6QFgrqFsP61Skr16xPlx/7MEgBsDpFgn+ZVfQ6uCS6P7hZCmG/9N9cJvcL4bqdUYKdLgAmtRsBs0BHiY8TqcBJgqg30mPRaJY0WavwjmxGY9zHq8KkJVK88mMvKg7Xvy6Te7TchCal5XwT0DIvp0+wglYpKR5EUnsTKYysZHTOaEHMIIeYQ+oS6W9/U58V/NqjC2pNwn3DCaVptHOETQU55DiBZUtT1W2sM+djmqIl/TvtZsY1oKHjSWrx0XlTbqzFoDbyx9Q22n97Ohps8i8LWR3ZZNr3e6eVmrfDI6kc8jrvtx9sA6Bval4N5Bz2C6/Jruurbq8i8P5Mufl082rj353vJr8xnYsJEugVJSssJCRNYmrqUcZ+Mq7fA1p8FP5P0m/Tl71IxxJggU4PHdguTAvknCyo5v3tIuylRtRpBselozblQW2RX24wUdo1GwKsZ4gadVsDmEBn+4mp2PzGZnSelIFvXUG/lOjqtxq3Ab0fx+i+p/Htdra/hol1Zio2GPLau0v6OiKjUWvl6R22Byy+2nWTuBT3rbXv8p+PZf2Y/kdULMIhxJEX7u1leyOw6WcSIxPqzHwDsDicfbjwBwP6sEo/9VTUFln2NOsoqytBpdJj0DX/ezhY0goZgs/S6zXozaYVphL0WhvhU/RPcdenrlMdnS6AZIMZPmjdc0PUCtBptgyKbIFMQhVWFjP5oNDqNDq8aiz1BEOgb1rfJQvF2p/0vqWxui6WiiopK2+gW1I216Ws77Xqzvp/F0lSpkOrZnn2pETSMjB7J8rTlyjxLr9G3qA5Lc4n0jVRqFDy+5nHenPpms86Lr8lQtzuceOk1vL/hOO9vOO52zLypPblzXDePc9vLykwNNncSBZVSkPSliS/RJ7QPExNrPXsfGvUQa06s4beM30jJkwKG80bP442tb5Bbkat4waQVptE7pDdvTX3LzafMVdl8suQkCQEJLJi+gOlfTOdg3kHOVJwh1Bza5ARmcORgdmXvAqQJKUhpW1aHleLqYmYPnM0nez4BQEDw8I6WWXND2wsWldQUpBkSF8jpGoVyrwhfrh0ey5e/Z5BbWk1ccPNWdaptDrdg77zR8zgv9jyCTEHYnDaSP0wGJPV2iDmEClsFG07WTtxb44Utr8qZHWMYGu9ZObo5E7emeO3qJDYcyePuL2qLrAyODaDceitHigdzUniMPdn7OVWci7/JRKml1CM46hBF9FqBMd1DWbI7C2hZcDbC34t1D44jOrDhic2zl/ZlXeoZ8sutboonvVagyuogJrD2x0SrEbhuZBy9IvzwNmrpHu75+WqMCwdEKoHtK4dE8/fzE4gKaL9J19C4QL68dSQiIoNj/zoVwVXOXp4Y+wRTu01lVExtQPnBUQ+SHJ2sZNIAPPTrQ24qq7N9ENdRHL7rcKutJ+T3LLss261QXn2cLDkJSL+H7a0gT7kzBZtT+m1+4JcH2HJqC9X26mapKTJLMxERGRM3xu13LjEwkX9P/zd9w/pypOAINocNH4MPA8IHsP/MfpKjk93acf387Mvdh06jw9vg7Zb2eCj/EDqNjnemv6Nsu23IbXy29zN2Zu9EFMWzNsW/rQiCwJK7RpNVXIVBq2FYPeMAmcGxAXx3RzIVVge9I1v2m9dR1B2j6NrRL1WnqR0HpBdUYHeKjO4WzJtX136nAkx60vNbX/OjIcotdiVDCWDVoVy3/fnlldgFSUFr1Bnp5juZgyXfMijWRGxgED/sPU2qSz0JOb2/yurwKGot32+tmmPoHVHsykxHwIgG93tvXnnjY8xqF4W33uW9k0UXVTYp2Oyl13Ki+ATeeu9z7ntldimkXVxdXO8ioFwAdsstWzz2/ZH0DOlJ6t2pyoJaQkAC61gHSGKi/Mp8wrzDOHTXIa74+grWn5SCyq5BabPOTGZpJnan3eP3ori6GD+j319W2ayiovLHEWIOIaMkg0pbZafMG+RAM5wb85QPL/mQPTl7cIpOpn8xHZvT1u7KZpkbkm7g1mW3UmxpeYFhnVbDsrvP43RJtdv2Wz/boWS516WowkpZtQ3fNhZnbbW8UhCEGEEQ1gqCkCIIwkFBEO6t2R4kCMKvgiCk1fyvRmSoDViOjx/PJT0vcbPR8DZ4K6sVC/cvZED4AILNweg0Op5Z/wwRr0cQ8XoE60+up0dwDyZ3nez2BXT1bM6vzCcuII5p3acRYg7h1c2vEv5aOM9teK7JPjpFpzKhLLVIKYLjPx3PwPelCUBSeBJaQRpMi4gEvxJMaoF7QQsvnRddg7q26j1yZdBzvwJSsZq4YOm1hvt5KcXlxr66rtltLd/v7m3jbfBmctfJDIkawsjokcr2Vza/Qte3uxL6aigL9y9UtrdG2Szjo41yW5WPr3kt7TER8PPSMyLBXQ1z2aAuDI6JhOokEHUs2PE2sW9F4P9Pf35K+8nD/8jmcDIgOgCtRlAmgLHBLbu5x4d4N5o2azbouLC/pHIPdlEtC8Di3Vm8vMJd3eal13Je9xAGtSKY6xoAntQ7jF4Rfu1awVqjEUjuGsyoru2nPlNRaQs+Bh/GJ4x3y1ow681M7jqZKd2mKP96hfRyO8/fy7+zu3pW4O/l3+qsG/k9m/7FdHZn7270WDnluyPSABMCE+gR3IN+Yf0YFDGIMmsZPeb3QGxGrrHcL9lbVCYxMJEp3aYQ7RfNhIQJTOk2hdGxo/E1+jIqZpTH/c5VuTH9i+lEvB5ByCshimo8tzyXLZlbGB8/3s3iw0vnxcx+MwF4edPLrXr95wrdwnwY2yOU5K7Bjf5GCoLA0PggxvYIbTBDqLPpE+muzGmPBXKZPadqJ0qX/1vyZrygTwRhfrWvPcjbQEZhJSNeXNVu1/1+Vyb9nlrJigM5yrayanf19Jdpj1NoeBuQlM1FRZL9yeHCHVycJGWDyF6IAO9vOM6Z0mp6P7mCd10U0iUuk7cCwxtkmC4n03Q9p7xmYhNqldEAvxx0D3jXxeYSbK6w1vZ36POrEEWR6ppg8/rMb/nywJdtsq/7o3C9n/SY71nM+b0d7/HUuqcQEBgSOaQzu9YsegT3UOZ1gyMHK9undJ0CSAHoIFMQV/a5Utnnmi3i7+WPQ3RwxddXuLUrPCMQ+HIgc36cg9Vh/Usqm1VUVP44ZLuzyNcjleJ3ncW5EGwO9wlnSrcpTOs+Tbk/d1Sw2aA10DO4Z6sFM93DfRnbI9Ttn1GrwVZPxhXA08tSGPjsr5xo48J/W+Q2duABURR3CYLgC+wUBOFXYDawWhTFfwqC8AjwCPBwm3r5J0AOWNa1MZA5XSZ5wl0/4HruHXEvOo2O5X9bzrHCY27HTUqc5HGuq41GXmUe/cL6AfD91d9z4MwBHl3zKOnF6U320eqwKgWnZO/LTac2KfvNejPbb91OTnkOGzM28uLGF9mbs7fettqLoXGBRAdG0a+LP+N6hrE9vfnVv0N8DOSXW90mBvVx7J5jdH27K0VVRWSXS4Fpu1Ma0M8eOJv/7f1fq/tvrmOfsWjOKE4VtZ/3Uaivkf9cP4Rqu5NKi50LB0QS6W/iq+2nEDAo3oMA9424z8NSxWp3KpYecrE/V+VMe/GP6b2Z3CeCfl1qA1zBPka3Fbald7W9qvglA6Ow2B1Y7U4m9m46pV5F5a/CSxNfYlLiJGL9Y8kuy+aiHhf90V065xgWNYwHkh/g9S2vk1GSwaDIQQ0em1eRR4BXQIcr0e4feT9LU5dyOF9SbDfl45ZXKXm/XtPvGhIDE6myVyGKIuPix7XougMjBvLVjK/Ir8xHI2g4lH+I+b/P50TRCSJ8IpQxjRxsceWWQbfw0K8PcaTgSIuuqdJ5PHNpXwbE+PPYYql2RXsqm9cfyXN7PqprMJcPdrdhke20cktbv9hfl8M1iuSTBbUTp5xS98mzXagVJxi0BkRrD/CC/KpcxdKrqNJ9TCm3u3DbSeaMk8QWeeUWELUgOJTj9M5YbJoMbMJp9GIMXQJMZBVXYWzCKszVeqPSWtteucWO1eGkqqZAYE6lZLWx8IqFnGvMTZ5LhE8ED/76IHmVeR6ZGvJcaMV1K856de+dw+7E5rBxcc+L2ZW9i4X7FypzC9eFt9kDZyuP7xt5Hy9vernBudqmU5uI9I0k0KTqt1RUVDqPWwbdwveHvmdL5hZyy3OJC4jrtGufC8FmV8x6MyWWkg4LNsvXaEth8LrotIJbwWYAXy8dvkYdU/tF8tGmE+SWVpMQ0jw3gfpodVRJFMVsURR31TwuAw4BXYBLgU9rDvsUuKzVvesAkt5L4rn1z2Fz2Oi9oDchr4RwKO9Qh19XVjbXLdAmI1eBv2ngTQyJklbtL+h6AXOGzXH71z24u8e5cgD74i8v5nD+YULNkvr3/LjzmTNsDkGmIMV/eOQHI7lt2W1EvBbBq5teVdpYuG8hKXkp+Bp8MWgNirLZFW+9N4MiBzGt+zQlaHnDkhsAmNF7BtB2H7Uf952m+2M/Kc8j/L3QaTWM6ym16+rnO+mN9RzKLiX5pdVsPprv0ZYs+1+Xeob+T69UPO/qkhiYyNRuU/kt4ze37UatkTj/OByig62ZWxvs8z0/34PuWR0P/SJVanVVQvsY3C0cgn2MDIxpvkdoc7igbwSXJEUxc3gsvl56RT2tF90nbzP7zfRI/bbancoESq4235yq8y1FViu7Eu6iYhoWH0hSO7wvPkYdN41O4PaxXZtV0EhF5a9C16Cu3DbkNqZ2m8pNg2465wZxZwNajZbbhkhexo0N9p5b/xzvbH+nRUX7Wku4TzjzRs0D4G/f/63RY0d/NJpbl90KSJ7dNw68kTuG3sGcYXPoHdqygmIaQcM1/a7hruF3MWfYHG5IksYCEz+biCiKyvsjZ225EmgKpF9YPz7e8zFZpVktuq5K5+Cl1zJrRBy9IiRbD207LkIn1pm0zBnX1SMDyXWs197YnSIL1h4l/pHlHoXHRKF2/Oal88KkkVRdBYY3uXhRD6zCCYoqrVQYv+K08R5ERN5bLwVCM4uqFI/DSqvNLdAMYHRK2SVVWqn45vXJccQGmRtUFcm4FkqsqzCyOUTF89rqqMDf6E9yjLvlzblApG8kD4x6gHcvfBeAnad3ApI3ZeJbiby25TUApZbM2YxG0HB/8v10C+pGj2BJpW1zSEp313o9rlYhET4RXNP3Grfi666ZKgfzDrL6+GplfqeioqLSGQSaApk3Whpjxr8V36Zs7/qY9f0shGcEhGcE/F5yz6g61zKIZbFHR9ZM8DH4SJnqL/pwz8/3tLk9nVbDioM59H96JX2eXEGfJ1dQVi2JF6f2k8Y/r61MZcqbzasLUx/tMnoUBCEeGARsA8JFUZSlATlQf8UeQRBuEwRhhyAIO/Ly8uo7pEPYl7uPJ9c9SU55DofzD1NQVaBUDe5I5AFEQ8rmN6e+yXsXvtdidRHg5sN7Wa/LuHv43W77jVqjEuzelrWN/+76L7kVuby08SXlmM2npFTGuclz8TX4UmYpc2vjsfMfY1r3acrz7kHdeXnSy/zf8P/jH+f9g9cueI2nxj7FZ5d91uL+u3IgqxSbywpL3cIQiSHezB4VD8DRM+WsOJBDdkk1S/Z4TljlAfzW44WUVds91DSuhHt7fkx9jb6Mjx8PoHhp18emU5twiA62ZEo+crkVtSmRyVETGzqtw2goXbc+xZvVISpBWdlGo70M4ZtC9uIG6KRLqqioqLQJOUjfWLBZ9uR8/YLXO6VPl/S8BIDU/NQGj3GKTjaf2szQqKEsmL6gRYURm8PAiIHE+sdSZa+i3FquvD8NLWpc1F1S1h8tPNqu/VBpX+ouRrcHX9+eTO9IP96+dhD3T+rBsHhP2wd5ktOeyGMbi83BqyvdvysXJ0URHWhCxIJRCOLVya8S6x/L2rnTlWPKrMVYNccpqrCRr/kcm+Y4IlWcKqq9F8j1Rkot0vjG2z5e2acTpcxBk17KePM2aDHoNPUWDXRF3j8iwfN9stqdOGuCkjqdpUMKE3Umcmam7Hn/wm8vcKK4fqHIuYBJJwUd5Hmaq8VGXYw6o1uNmCp77Rj5geQHmJs8l0fPf7SDeqqioqJSP8OihimP5SyN9uKL/V8oj+WseoDvrvquXa/TGfxryr94IPkBnh//fIdd4+lxT3P/yPuJ8o1S5hptQa8RyCuzUFZt59rhscwaEcvtYxK5ZlisMu7bcbKI1NyyJlpqmDZXrREEwQdYBNwnimKp6yqEKIqiIAj1hpFEUfwP8B+AoUOHtluoacupLTzwywMsmL6AQZGDeGXTK4r/rusq8ZTPa1M7ZQ/DjmBRyiKe2/AcBVVSgcCGlM1+Rj9uH3p7q67h2ubdw+6mT2gf9/06I5W2SqZ+PtVte1F1EcIz0t8rwCuAHsE9GBo1FD+jH18e+JKNpzYC8MjoR3h+gvTFWbD2KEHeBq4dHqusdMk8Pe7pVvX/RH4Fz/2YwoK/DXZTeEzu4xkAFgSBpy/py8jEIO74fBe7MiRFx88Hcigot/KfG4ai1QjYHU4y69hVbDiSx/zVafzfRE91+Ji4MXy691O3bUXlWryEWKA2sLA9vZBnlh1keHwwT14svc9VNuk6p4olH0I5UB9ifZiJvWJb/oa0EW3Nd1AQ3VfW5EGvTGm1jUPZpYrKSLbRsHdS5NfXq/b201kBbhUVFZW2IAdPH13zKMO7DOeWH25xCxDcP/J+8ivzuaTnJUoQuKMJNgcze+Bs1pxouDiv/Dt1ac9LuXPYne3eB51GxzPjnuGmpTcx4oMRSqCkIYXHZb0u45+b/tmsdMAfUn/gybVPIiJi1pv5asZXnZrK+VdGttnStKPCKNTXyM/3nt/oMSE+RmaPiueTzelkFVfRpR0K/crexm+v8VzgmH/tIKbO/xFb9Snivabx4KgHAamGRbApWBnDFxjeZHflRqjRQZTqlnKq8FqlnYvnb+Sne89nV4YkbjA4u1LtTMGhyUUjGvHRhRJq1lJRKdWzMGg17Mko5tbPdhDmayQqwITN4eRfq9L47o5khsYHcTxPUjPPHhXPthPuVnJHz5RjdzrRagTKbWX4Gs+OApOtpXeIlGEx79d5fwpPd7lIr5zl0pgNhkFjcFMNyvfG+dPme4iIVFRUVDqLKN8o5fGc5XP4edbP7dLu6uOr3Z5H+kSSXZ7N/w3/P2b0mdEu1+hMrup7FVf1vapDrzEhYQITEiZQXF3Mz0fb/ndwFSg+Nr03GhfLtP2ZJW1uH9qobBYEQY8UaF4oiuL3NZtzBUGIrNkfCZxpWxdbxuLDi9mSuYVfj0sF5r468BV5FXkkBiYqhesCvQI5lH9ICdJ2ZLB5aepSjhYeZWjUUO4cemerCxM1Rqx/LHcMuYPrB1zP8C7DPfYbtUbSi9NZeWxlg22MihnFA8kPANIK+tj4sSQGJjKj9wy3ghavrkzlH9/vb9f+v7A8hTWHz/BbWp5bumBjhd26h0sD6qyagHJZtZ3Vh88o/swFLj7NF/QJV1JBf9h7ut72JidOZma/mVzbr3bSYHT2YMV+aWAvD/p+O5LHgaxSvt+dqRwn7ztdIn0pZQsSjWjCVEeZ3RnEBJnoFeHLkIDb8XdOJNTyJNf0voX4gHi3447U+AzKPjzyDcfehMqmvXjswt5K0aF7JnbrlGuqqKiotIVAr0ACvAI4U3GGNSfWsP30dmL8YkgMTCSjJIPFhxeTX5lPiKnjLTRcMesa93FrSmncHkxKnMTMfjPpGdKTgREDuWngTfQN7Vvvsc1RiMssS13GkYIjBJuC2Zq5ld05jRdnVGk/bjkvgen9I5jYu20Waa2ha6g0Njmc7Wnr1hpK6xQDrIsFyQ4j1MtdkCAHmmWqtNuVx1aNFLj2N0nj1aziKpbtPU1RpRQgFtARYP8bZvv5eDkH4+flhUOU+mHQadDrNJwuqebXlFwWbsvg1ZWp/GtVGgDvrJXalj2io+oJuL/w0yHsDhGdRqDMUnbOK5uDTEHMHTmXYV2GkRiY6Lbvg4s/+IN61Xp6hfTi0fMeZdHViwDcisPXxagzutlodMY9W0VFRaUpBEHgnuGSZcNvJ39r4ujms+LoCuXxjUk3KlaxOk2btbB/ekLMIeRX5jerMHhjyOplQcAt0Oy6r620+q8pSBLmD4FDoii+4bLrB+BG4J81/y9tUw9biPwBtTlsfL7vc3bn7Gb2wNl8fOnHAAx6fxCFVYUUVRfxxYwvmPndTJ5e/zQioqLMXXxoMTuzdypq3tZypOAI/9v3P4ZGDWXxNYvb1FZjaDVa3r3o3Qb3G7QGpbiG/OEEiA+IV4pRLLlmiVJ0467hd3HX8Lvc2nh//TGSu9Z6je3KKGJwbOsLVXzw23GO5VXw3KV9WXVIWo9YfyTPTeEa5N1wsDk+WJqEHK/jXycrV6pqiqi8cXUSVwyWUhf/8f1+vt+Vydxv9vD9rixGJAQR7ufFExf1IcY/hi9nfAlAWsFRdmRvx89+Gd/uOAMCvLT+Xf67dTVeldcDgcp1Vh9fraT72TWZvL72J17deTMAAia8DJ0fbBYEgRX3jQHGsPrQDG75dAePjhqNVlPbl9PFVdzxueSJJ6erygqmToo1MyA6gGMvTm/6QBUVFZWzBEEQeGbcM9y74l7uW3kfAEtmLkGn0THps0lsythEcXVxp/g1u2LWm8mvzMfmsHkU0Mopz+HO5Xcqx3UU0X7Ryu9oU8j9eGnjS3yT8g0vTHiBbkH1LzrmV+XTLagb7174Lr0W9GrX4igqjTOtfyTT+ksiid+zfuflTS/jFJ2EmEL494X/7tBibYNqxphVNgfPLkvh3ond8Te7X2/DkTzOlFm4ckh0o22VVdtYtvc0VZq9lOl+xOQYCIYjCNaBOIQCLv/6AzYVrQIg1ty46toVEUmJGhVgUiw0Hl9ygFE95CP0+Dgm4uOYyOxR8XyebkC0S+NHrUbA2EhtCXk8LI83owNrg82jugaz+VgBWkHyba7W7uLX478yOXFys/t+NiIIAq9PqbUf+mTPJ9y09CY0goZbBt/yB/asdQiCwAsTX2jWsQatgeLqYv626G9c3fdqLv/6ckANNquoqPzxvDXtLUK9Q3li7RNYHVY3C9eWsuP0Dl7a+BI7T+8kxi+GjPszAHh98+tsOLkBrdD5sZNzjRBzCFaHlUu/ulSJ79w59E4md23ZGEBfY6FaX8xa307B5rYom0cD1wMTBEHYU/NvOlKQebIgCGnApJrnnYZeIw1EbU4bC7YvAODiHhcr+41ao1IlPcQcgs0pDQ6fWf+Mku50xTdX8MJvL7itMLeGn9KkQnfTu/2xAbXLe11Oz5CeJEcn8+y4Z0kKT2Jy4mTeuEBaIxgTN6bRCYPTKfLSz4e55J1NyrYf9tSvEG4uzy8/xJe/Z7gVO8kqrlKUzX2j/Bjbo2EljVYj1KsarpKDzTX/ux4zsVcYYX5Gvt8l+TtvO1HID3tPs+W4u2rliTHP4eVIQi/G4GMwEKmfjM0BR8t/4XS1VCiw2ubE7nDy8R5pEcPkkPyMnl7/lOLZrBHN9I74Y1UmsnK4rjXGlmMF5JdbiQ82k1ijHrpsUBdGJgZxUVL7q+9VVFRU/iyMjRvL0KihynN5kfuqPlfRxa8LA8IHMKXblIZO7xDkehBHCo547Ft7Yi2LDy8mKTyJkdEjO7VfDRHtF83kxMlYHBa+OfgNy48sb/DY/Mp8QswhLVJDq7Q/3x78lsWHFrM7ezcf7P6g3s9aeyKPX37Yc5qPNp3g5ZWHPY654aPfefDbvU22JRfxq9Cuokq7hULDuxSymnKv97GYvmP5keXYxHIAth21uZ37y3W/cPPAm3n8/MdBdJ82ycHmyXWU3+vTTgFwaVIit49JZGRiEFcNjUav1RMXbGRkYhBD4wMRaViVJL9+WTxhMmi5Y2xXLk6K4rELJbuJsT3CsDudlGqkbM7Le13e5HtxLjGiywiGdxnOo+f9ebyK/2/4//H1lV97bJ+cOJmeIT358sCXSqAZcPutUVFRUfmjkEUUBZUFTRzZOF8f+Jolh5fg7+XPrP6zlO2X976csXFjmT1wdpva/yswPn48Q6OGcrLkJMeLjvNz2s98tOcjZf+zl/blnnpsY+sS5le/vS/U1vKSaa2KutXKZlEUNwINhbw7vypauENt7QAAIABJREFUDQ5RGpS9uvlV4gPiuajHRVzR+wplv0FrwO6UUtjqKo8e/OVB7h1xr/J8xjczuG3wbVzcUwpW7z1VzI/7PIOsBp2Gm0cnEOzj/geTJ0SPj3m80T4XVlj5ZHM6Vw+NJjqw/Vew70++n/uT71eezxk2R3ksPtX4B2fPqWKW1WM98cnmdAbFBnAou4z+Xfy5cEBtgHLpniwEQeCSpCiP8wDSXEzGP9zoXvjD6nCSGOrN8nuaVpbcen6Ch/feN9tP8fhFfVhzWFJLuyqLJ/UJx6jXcP2Hv7uds+JANl0CTKw4kM2FA6JI7jKOcKukRFj30Di89BPJKi4h+q0ACg0LsNmyEBC4f8VqtmRuIc5nAM68RzhlmkG5sENpd0RCCBH+Xk2+jo5EvlE4nSLH8sr5Yc9pbh2TiN0pBfUX3joSs0G6DSSEePPVbedeFXMVFRWVziQpIontt25Xah7I3D709lbXXmgrchD54z0f89oFr7ntW3RISuFedcOqTldcN4RRZ+SX63/BKTrRPqvl64Nfc+/Iez2Oyy3PZWPGRq7qc5USbP7ywJcczj9MSXUJ/l7+aAUtdwy9g4TAhM5+GX8pLA4L/l7+vDP9HS7+8mK3AmYdgbZOSufuDKkuxsqDORRXWpnat/kL43aniJ18KnRr3bZbnOXghOToZKXIswb3YsqTu05W1EKf7dhERpXUxvmxY/ktQyrQMywhCIuQRqV2A6BHqDF1/tvQfkxK7K20pdfo8faCr26UxloTeoWzPV2qPZKcGMyXt41k6PO/kl9uJaOgkpd+OsSOk9J+L52WR6b1AlCEGW+uOsK1w2MAO/3D+ruN7/8M9A7tzba/b/uju9GuvD3t7Xq3T+s+jSFRQwh/rbZezdKZSxvM+FBRUVHpTOTx44LtC9qU/Z9flU8X3y7svcN9oTgxMJF1s9e1pYt/GYZEDWH7rbWWXskfJrstAtyQHN+sdm45L4Hf0uq3E65ro2F3im5q5x/3nWbvqeImr/GnM0WxOSRFQqWtkpS8FHqF9HLbPzJ6JLuydxHhE0GcfxzzRs3jlc2voNfoeXPrm24+MT8e+ZHU/FQl2Pzf346zfH+2m1rWKYpU25zEB3tz1dAYt2tVWCvQaXRNphn+fCCbt1enUWW189iFfRo9trN5YXmKMhCWGdczlHWpedz71R5A8qpzDTbL2xsKNn++9aTy+Kvtp5THFpsTjeBots9xt3DPQigLt2Xw+EV9WFsTbJaL38kEmGrTPp67rB9PLDnA4ewy3l13jFWHcskorOTJi2s9Jo01ldiDzD5oRF+cQhll+iUIohcf7dUiAENCxpGe555OIqAn1OT+efgjcFU2v/5LKj/tz6FfF39sDmmRQa9pv6I/KioqKn8lLu156VkTvJUDsa9veZ2XJr7kNu5IyUsBJL/psw2NoEFA4MCZA/XuX3xYsiDrF9YPfy9/+of1Z1vmNn47+ZuSmQZS8PrZ8c92Sp//qsips/JnrcJa0cQZbUMuSmioGYeVVUt/79v/t9Nte3PQCgIVunWAlIngpfOi3Fqu7J/ZbyYHco9grY7k9asGN9jOY2Me5PaVa4ny6YKx5js2tX8I/aL8KdV/R6V2k9vxdQt267V6ZZ4C7nZx8nht3pRezFu0j+P5Fby/4Thmg5YhcYFufoquE74VB3JAsLUprVnl7CDYFExSeBJ7c6UgTHK0KgBRUVE5OxgQPgBoe7C5uLqYAK+A9uqWClK9g9zy3Bafp2/EyivEx0h8sJn0Akk8a3eIuIbonv4hheJKa5NjsT9fsNnpnv4mFwGUeWXyK7wy+RXl+cuTX+blyVLF48CXA1l1fJXb8afLTvPx7o+Z2W8mVruTnuG+NX64Erml1Qx56Ut+OvEp+WKw27nbsrbV67W15VgB/aP98THqWHv4DBuOSFWrO8srtyWU1VNQ5ePZw7j1sx2K13JJlY3c0mrC/ZpW8TqdIp9tPUmQt4HZo+J549faNMwtxwsIMOvp38W/WX2bVuM1LDMg2p99mSXsPFnEjpNFTOgVRlywe7DZZKj9Qlw/Mo5D2aUs23Na8X7eebKInTUqkjeuTkKomewYdRrCLM+R4yV5dMZWf8f/Zg2nR7gv57+yFl9jbbvWxx10f+xnfI0+zXodHYm8KrVoZyY/7c8B4L8bjis5CbpGbjIqKioqKg2zZOaSP7oLCq5jjX9t/Rch5hBm9ptJSl4Kh/IPcf2A6918+88mnhjzBM9ueJZ/b/83Qs2P0/lx59MvrJ8S0Lxv5H3oNDr2zdkHwFtb3+K+lfcxvMtwjhcdZ8PJDfxn53+4ovcVbVoAyCrNYtmRZZRZyqiwVWDSmfA2eHN+7PkkRSTxU9pPnCw+SYg5pMOrjtdHbnkuSw4vUWzfZLoGdeWCrhd06LUtDgtGrVH5rH2X8h0peSlc0PUCpQB3e6KrCa6eKpQmOplFVayvGS8DrDzQ/ImVxe7EojkMog7r41YEQeD5Dc/zxNonALh7+N3cM+KeJtu5beR0bhspLda/vvl1Vp1YxStX9sbXoMdh2MHQsKHsyJYy3IyOXkT5uosu9Bo9y9OWU2opxc/o51YIWw42Xz0shld/SSWvzEJMkInf5k3w6Ic8NgXIqTpEmdcBjLqBzX4/VM5OtBote+7Y80d3Q0VFRcWDHsE9mDtyLu/vfL9N7VjsFrx0f2zm958Ns97cqmwzXSOiQy+9lnUPjeeD347z/PJDLNx2Eh+jjukDIvHz0lNltTN7VDyPX9QH4blGrtHiXp3l2Bw2tIJWsdOQfQybQ9/Qvmw65a5KqLBVcPMPN+Nt8MbuTPCQlOs0AsW6L/gkZRWfpHi2mRSe5Pa8oNzCtf/dyuQ+4bw9cxA3f7q9XlPusxlBEOgW5qsEm0Eq+NccVfbuU0WIomQdUp+CubjS1uz3Q6/VEOHnRU5pNQCTe4ezL7OEGe9uBqCgwtNz26vmmvI4vUeYD2WW2oB6frmV11amAu6VvzUaAZ0oefIFG7pDlaQmue2znVjtTqx2J2FBsZSVR3A8T5ocW8+C1QO56N+3OzOVbb+nFyqP26vSqIqKiorKH0e0X22BtHmr5gHgbfBm1veSH55GOHsXFmX1510/1RYmPj/2fDbctEEZPJt0JrdzeodK1gTDo4Zj0plYf3I960+u50zFmSatyxrjlU2v8Pbvnmnuw7sMZ9X1q7joi4sUn920yLROT3F/c+ubvLzpZY/tOo2OykcrO7Rgn8VuwagzEu0XjV6j553t7wCSV/k3V33T7teTg6/H8moV1Dd+VGuDtuJgTrPbCvI24BDyQLArgdreIdJnqGdwz1Z9P1w9xPMq8rA4LHgbvNGL4diEXPRirMc5Yd7SOHLhvoXMGTaHxNBaUcL0/rUCiphAE3llFrqHeWbw1UUWQdQV16ioqKioqLQnclBTFEW3Rc+W0NYCgyqemPXmVtUzaY7osEtNPOz55YcAafH+huQ4qmwOTIamRSznTLC5ylbFplObcDgdDR5jcVj44sAXBJoC8TH4kF6cjkHT/A/zmhvXUFRVhFFnxKg14qXzIq0wjZ7v9OSbg99wMucqQrzdC77ptBqsmmPE+PRh++1rPNqsmyaQXy4FQH9NyaXSakcU4aEpPXlv/TFO5JdzIr+CkwUVjO0R2uCXuNrm4EhuGQOipbYPZJUQ4e9FiE/rB5qVVjsHT5cyODbQwyevPh6e2pNbzkvA36Sn71MrsNT4x+WUVDd6XkF5bQDY1U+5V4Qvh3MkL+dp/SM8zmuIdQ+NQyMIWOwOfL30rEzJ4UBWKQA2u2ewVw42y4Hu2aMTiPA3ccfnO5VjyqptTO4TzshEd6X6qX9eS07ZNILMZkb/cwPpBRVKIcJbz0+gb9R67v16HwdPlwBwQZ9w/mgCzY1//vWaszcAoaKioqLSPKL9oil9pJRqezX5lfn0+XcftmdtV2pUdLS/blu4pt81TEqcpPT1juV3sOTwEtIK0jiYdxDAI4h6QdcLKHq4CH+jP3anncKqQuLfiqfUUorD6WBjxkaq7dXotXoEBLeCzz2CezTo71xuLSfCJ4KccvdA5snikyw+vBgRkRm9Z7Do0CLWp69Xgs2/Z/1OUVURvUN7E+vvGWRsD+xOO0sOL6GLbxd23lY7Zvlkzyc8svoRCqsKCfdpeNyRVpBGYmBiixXudqedjRkbOVV6CqNWCjYXzCug0lbJFd9cwZ6c+pWYqfmppBen4+/lz4guI1o8MZXHouUWzwy7QLOeokqbx/b6sNqd7M4oQsTB5MRpyvYZfWZQMK8Ab713I2c3jBxsLrOUUWaVxq/3jriXxVdfwIBnF6PBM0vvu6u/w/i8USkk3TPCl0PPTkUQasenAF/dlkxJlY1Ac/MXD9TJu4qKiopKR2LWm3GKTiyO1quTLQ5Li8SgKk1j1plJL07HKTpbtHiub4bocFr/SHY/MRmb08nIF1ezO6OImCATTtF93NIQ50yw+e1tb/PI6keadWz3oO7EB8STXpzeonRKg9bgMVDvGiilBkoFdhYhVJuBWpWFXitg05xAI/RvdJAvs/V4rXn3pmPS40CzgS4BJtam5rE2dR0gVZFsyNz7X6vSeG/9MVbeN4ZAs56L5m8kKdqfpXef1+zXWpfXfznChxtP8MlNwxjXM6zJ4wVBINRXulH4mwyKB/ClCzYqx9S36lVUKU34As16QrxrB8YR/l5KsLklRRLlD7nsFxPsXXvzGhDtOdD3rimGd9mgLsq2XhHuypGiShsRDViCRPhKAf5uoT5sOlr7t/Q36YkK8ENAy/J92TV9+eMH/iG+nn3oFubD0TOSV6GqbFZRUVH5c+Br9MXX6EuQKQiALw58oezrFnh2F5kKNtcu7vYI6gHA2E/GUmIpafAceTFfr9UT7hOOt96bSlslK46u4KIvL2rwvO5B3Tnyf0fq3Wd1WjHrzYyMHsnWzK3K9tyKXG5cciMAkxMns+jQIv6+7O/cPOhmjhYeZcQHIwAYEjmEHbftqLfttrLk8BJSC1JJCk9yG2/KgfP8yvwGx6Gp+an0WtCLp8c+zVPjnmrRdb9L+Y5rF10LwJg4yUZO/qzJogyH0+ERxE7+MJmiasmWbP+c/fQL69ei6zYmfOgV4ccWl/F0RkElscH1jx2fXnaQVYfOIBotBJvcfcvl70prMOkltc+WzC0kBiYC4GPwIdDsjZZAxvcM9TjHoDUQZAoiv7K2IE99yiCDTqOMsRtiZGIQW4/XZqrVtRFUUVFRUVFpT2QLrz05e5TC1C3FYrfga2g6a0el+cg159anr2d8wvgWnNe8wHRgTUwrKsDEkj2nWbLnNAAhPk3Hus66YLMoipwoPkGkTyTV9mq8dF7otXrWpq/Fz+jHilkrGjx31EejANhw0waMWiOpBakMjGibh5lWo2XN9ZuZ8D+pbVGo5GjhUQDi/OPQ1qwehOiTcDpFtwIe9WF31npEnC6WlEZ6rcD/bhnBP77fp1hTrE/Ncws2i6JIcaWNQG+D4lm3N7MYf5O+5nEJZdU2iipsRAdKA+DMoiqiA01N9glgV0aRco4rsnK3MfRaAXuNZURuqUXZ7nCKSjCzwmKn3GJXVMerHxhHgEnP30bE8sW2DEWiDzCme+v9FuUVGrNBy9OX9PXYbzJo2f7YJDe1SHyIN6vmjuG3tHyeWZaiHNcY7143mNScMq75jzQZ1Wk1DI6VJr67aypzBp4FwWazQceGh8az8Wg+jy7eD8Cyu8+j95PS96gxrx4VFRUVlXMPrUbLxT0uZmPGRsx6M6NiRrU4wPhH8sz4Z9h3Zh8rjkq/U1f1aZ43sllvJqc8h13ZuwD4asZXzFw0E4ClM5cSag5lwfYFLNy/EIfTQamllMA6wUeL3YJBa+DnWT9TYa3A4rBg0pnIKMnAKTox680MCB/A9tPb+XD3h+zJ2aMUYAz3DleCqx1BYZUUWPzs8s/ctsuiir25e0kITKi3VkhaYRog1RKpjzJLmaK2rYusXF5zwxqlQJDMpIRJrDmxhrzKPCJ8arPSHE4HRdVFDIkcws7snW5V0pui3FpOtb0ajdDwZNTV5gyk2iGl1TYKy610CTS5Fb1Zn5qHk0rsmmx8ja1TMdfH2LixABwvOq5MwOX3fufjk/Dxqn+KFWIO4WDeQY4VHiMhMKHVFjf/u2UEuzOKGVHzcbhz6J2takdFRUVFRaU5jI4dDbStQPCp0lN08evS9IEqzWbOsDm8s/0dNmZsZFz8uGZnkjVH2ezKojmjlFihXivQJ9KviTPOwmDzq5tf5eFVDzMmbgwbMzaSFJ7E0KihrDy2kr6hfUmOabgyb7ApmIKqAmXA29oVF1dEUeT+/xW7bes+vzsANyTdwLvT3wXgWK6Of687yt0TujfaXrVL8LaoxlNYVjD0jvRTgs11FR3vrDnK678eYePD4zmULQVs5323z+2Y/k//AsDcyT2wOZzMX3OUeyZ0Y+4FPRvtk93hZHeG9BpXHszhupFxgFTM72RB0/4vOq2A3SlSUuWuqrA7RXQ1MdvJb6zndI3FhkGnIagmEDs8PogvtmUoz4FWewBB7QrNuJ6hDUr761OLdAvzVdS+zSHAbGCEi82Gj1Gn+N4U1vxdm1KldBaxwWaSLJLKO8LPC5NBS59IP1KyS9v0XquoqKionJ0M7zKcZUeWAZL/8bmUYu+l8+KKXlcowebmZqgFmgJZdGgRiw4tQq/Rc2mvS5V9kxMnY9KbWJe+DoCI1yPIr8yn4tEKt+Cs1WHFqDUS4BXgZoMW6Rvpdq1JiZP4cPeHDP7PYGVbt6BupBent/TlNhuLXVrMr1t0Tn4+6/tZJEcns/mWzR7nllmkzDFfY/0B3JEfjlSC5vXhY/CpdwLTI1hSod+34j6+uvIrZbts25IQmMDO7J0t8hLs9U4vssqyOH53w57MMUFSsNnfpKekyobF7mDCa+vIL7dy9dBoXrmytl6Kr5eO08a7AU9ru7YQbA7GoDXwzPpnlG1y+8GN2Nr5GHxYl76ObvO7ScUxxz/bquvrtRqGJ9Qqs7sHNz7/UFFRUVFRaQvyeMnisDRxZP2sPLqSnPIcRYmr0j7IdVueXPckXfy6cPOgm5t1ntko/R2aY6ELEO7nRXgD2f8NcVb9pUVR5EiBlNq4NXMrTtHJ7pzd+BmlqLnrQLY+Uu5KobCqsFkKY9GlCl1jAbdKq4PiKifUiChMjmH858p7eHnTy6QVpFFhk1Z2BNHIifxKnDXKZUGov91Kq3sxOgBjjQXELeclkBDizdxv9hIVYFL6KAgCi3ZJBd5yS6tJCPHmRH4Fb14jDaa3HCvgmx21BeByS6uptkkqi9ONeCg7nSKCABWW2gC4IAjKdYtrgsfD4gN57rJ+TP3Xb/W2o9dosDqcHn7NNocTL70WURTd+vHJTcOUx1P7RfC+YQijugYza0ScWzC+NchK6uZ4yNRlfK9a+5D44OapX764dQQpp0uZMTjabfvjF/Zuk4d2e9Mn0o8PbhhKQqj0ur66fSRZRWevh6eKioqKSuu5e/jddA3siojItG7Tmj7hLGPWgFn8dPQnlhxeoih6m2LhFQsVFW58QDxeOi8237wZu9OuWB7ckHQDj655VLExyC7LVmwoBIRmewle3utyvrnyG2XCFWQKYlnqMmUM296Ioki1vWbBvs7CQc/gniz/23Le3Pome3L24BSdCEhjIbmYoWxH4mfwQxRFZTtIxSOzSrOY0nUK1w24rt7rdw/qXu+Y9uKeFwOenuBycDnULFlJlFvLFfVvU2SVZdX0uVYlnpwYzI2j4jHoBPxNBrqF+ZAQ4o3DKTL3m73kl1uVMfWZMvdJ8Khu/qzceYa+of2YN3pes/rQHHQaHWtvXMvxouOAFGiWi102xiU9LlHU96fLTrepD/J72je0L/3D+repLRUVFRUVlcaQxx+udTCag2ytKmdZPXreo+3et78yfkY/Vt+wmomfTSSzNLPpE2roEmDi05uHE9aBAsmzJthsc9joPr87J0tOAu4f4vUn13NRj4ua9HsL8w5jd7qTqa/9BMChZ6fWa4dgtTsZ/uIqiitt9Az3ZeX9YxpsUy5M0sWnB1nlR0gw3Mx1A65jedpyvjrwFWGvSQFKDSYW7cpUgsLDE4L45nZ3FXa1zcGCtcfQCOAUUY411gRGA8wGrhgczSsrUvlkczqfbE5nQq8w+kb5kV6jMJ7x7hYAYoPMXD5ICnAOjQtyCzYv3JahPP5uZyZT+kYwuU6xuqd/OMgnm9MBSfUhs+FIHv2eWknfKH9+T5cmeNeNjKNXRMMy+eP5FRzPr+BAlru34vojeVw0IIoKq3sAOdlFEeyl1zKlb0RNP9peRX3j0Xyl3ZZi1Gm5eXQCH206oahmmmJU1xBGda1VXXkbtFRYHVySFNXIWZ2PIAhMcvkM+Hnp8YvsuKr1KioqKip/HAFeAVzb/9o/uhutxqw3c/uQ21lyeAnh3s0rttsvrJ/HOLFuNlyUbxRGrVEJEnebX+tlPTpmNAatoVkqcKPOyFV93e09fjn2S6vVPo2RUZJBj/k9lLaNWvdJgSAITO8+na2ZW1l1fBXaZ7X4Gf0waA1u3sAgTUhuWnoTn+79FJDe57137MXisDAgfECDweaGMGgNDO8yXFFdy3x/6HsA5W939XdXt6hdACe12XI9I3yZ2s+9ePSlA7uw86Q0TnUt8mx3iG7H7S2Q5gR3DbuzRXVcmsOomFGMihnV4nNk2vJ52XF6B+d9JNVqubzX5WqmmoqKiopKhyKPP+r+5jfGu9vf5Z4V97Dt79uUsUH/cHVxtL2ZkDABg9bQokwygLE9POtLtCdnTbA5rzKPkyUnuaTnJRRXF7Ph5Aa3/ePjm2d2fdAl4JlbWk18iKdCtaTKRnFNFevU3DJsDqebv5srcrD5oaHvkFKwjbtHXAPA4+c/Tu+Q3gCk51lICroSnD4AbDyax77MYo+28mrUFlP7RfDTfik98LHpvRmZEOx23J3ju/LkUqkC+5rDZ7A5PNUgGYW1H6ToQBMDYwLYc8rzmgD7M4s9gs17XfpXVi29RkEAUYQKq4Pf0wvpE+nHJQOjmFCj+F00ZxQ+xoY/MrLlxr0Tu/PW6jTluWwXItORA2L57zp7VHyrzp83tSf9o/04r1vrJiSf3TKCo2fKCGthioGKioqKiopKLRMTJjJ/2nyu7tvyQGVDCILAFzO+YHvWdk4Un1CUqKtPrGZr5lZ6h/Qm1j+2VW0btcYWTcCay+H8w25ByYaC4bcNuQ2j1sjxouN8tOcjQBo7j4sfB8BT657CpDfx+9Hf6RfWj/Njz+fdHe9yKO8QVoe11VYrBq3BQ+V0pkKyhLt7+N2E+4Qrz5vDj0d+ZPvp7Wg0tUKFhupLGLTuwgJ/k95jzHymMh3AY3Hgj2JM3BjeuOANHvr1oRarw1xJzU/F4rAwd+Rcbh1yazv2UEVFRUVFxRM586slC6Xf/397dx4fVXnvcfz7y2SyJ6wJBMImm2yirEVlF4W6QEXrUrdqFbXW1lbb+rK01qVVe3txqdelKi73WmuvtQJF8YoidQEFDAqyC0gQWUqEhKyTPPePWUjIAlkmM2E+79eLFzPnPOec50nCw5lfnvP7rf+7fJU+rdmzRpLUJrENaTTCJMWb0uBgc7hFzXc6uPrisiGXaVneMi3dvlTtkjoov8RfVCRYiKM+JeUVevjtzaH3B0tqr8x8ZKqGCX9YonOGZuv2aQNqtC0MBGIHZ52kH4+fEto+KGuQBmXVLEAn+XMSf7wtX2MfeFu3ntlf00/2J0EPFuH7zik5oWDzteNOqHH8lIGdQsFmSVr+xX7ltEtWXn5xaOVsapUV22amGyf01nUvrKxxLkl6Z8Ne/WhyX1385DL9dEo/pSfFh3I0V9W1bXK1AoEnd2+r68f3Dr0f3qNdjWNq88OJffTUv77Q40u2qMxXqRMym68gy7Hq16lxVU6TvJ7QivHGGN6j3TF/nQAAQO28Hq9uGnVTs5/3/AHn6/wB51fbluJN0dLtS7Vmzxqdkn1Ko86bGO9fMd3roV5qm9RWi69YrPbJh3PqFpUXadJzk7T70G5dN+w63T729hrnuPIfV4YWW2z7Zpt6tu1ZoxBPXb+wz8nI0R3j7tBnuz8LBZun9Zmm2067TZJ033v36ZGPHlFhWaGuHXatfn7az/XYisd03kvn+fvvadxjlImexBppNEp9pfKYRx1SOuj6Edc36HwjuozQ2S+erVJfqfpmpWnTnkJ56ihikxBffaHIgeJyLd+6X2t2HtDgrv5aFcu/fkOeuPRmX9XcWInxibplzC16bvVzoV9O3Lv0Xj31yVOhNsOyh+mV775S6/HzNszTg8se1MWD/cUvfzrmpxRbAgCEXfA+4cp/XKlHPnpEc86ao9O7n16tzT1L79Hsd2br4akP6/Tup+utL94KHSNJ0/tPF8IjxZuiublzNX/jfF048EI9MOWBSHcpeoLNwZvp9MT0UCqNPm1GaVNBR1XYQfVpd/Q8aDv2V4/kB1clH6m4SrD57CHZ+nTnN3rr8921BpsPBc6RWs+K3iN9e0hnbdt3SK/m7tRHW/eHgs3BFb79OqVpzkVD68wLnFwlBcR3TukqM+m8oV303qZ9GtsvUzv2F1UrCiJJo0/ooMu+1V3/vexwCo2Zw3L0yqo8VVQ67S0o1crt+brlr7mhAHKwsErQAxecpIfe2qTlW/fX6Ed9Hr10mH744qrQe6/H9POpJ+qxJVu0ZMMedUzzB2/PHdpFUwd1rus0zWL+Tadrw+6CsF4DAAAcP2YOmKn1+9bLV+nTrOGzGnWOiwZdpLyDedpZsFNvbnlT6/auC1Vul6Qv8r/Q8p3LJUkLNi2oNdg8b8M8Zadlh1ampHhTNK7HOGWlZCnFmxKqYVKfAZmH72WrFj+8d9K9yt2dK5PpB8N+UCP4eiy5qmuTGJ+ob0qqL2Aoqyhr/PkXdIKyAAAYrUlEQVQ8h1dOBYPJda1s7p2ZqmtO76WeHVLkq3T67Xx/kcNVX+aHgs1OlfJaSq3HR1KCJyG0Omz+xvkqryjX5BMm69Pdn+rVda/KV+mrdfXX+X89XxWuQpN7TZZU/XsMAEC4dEnvor7t+2rT/k1a8dUKzVowS2tvXFutzex3ZkuSbn7jZj15zpOS/IVxC8sKJUkdkqs/0Y/mc+f4O/Xejvf07rZ3tXDTQoLNVVXNRVfh/MHgvbuHq43vW5Kkob99W7dPO1GzxvdWqa9CP3t5tfKL/MHbMSd00ImdM/TI2/6k47ed1V9/WLSh1mJzj7+7RRWBIn7PXDVCk07spNn/WKMXlm1Xma8ydGNbVObTrX9bHVqBXF/6iCP16JCq+y84SUs27qmWO644ULQvKz2p3tWzVfMNz7no5NDrCf2zamsuyR84vmfGEP1tRZ5KfZV67upRGt8vU2b+vM3BFB57Ckq1ZONeSdKPJvXRPf9cJ0n67ogcndq7o4Z0baMhd74p6diDzWeflK1Fa7to3mp/oRMz05Wn9tRHW/dr/dcHVR74Gtw9fZDapjTuMc1jNSSnjYbktAnrNQAAwPGjV7teeuq8p47esB6DsgbpmenPaNWuVXpzy5u691/3auH3Fob23/z6zZL8eYyPzKX89ta3tSxvmYrKizS9/3TtKtyl51Y/p0sGX6JfjftVg/oRHxevK4ZeoedXPx8qjChJt4y5pVq7qoWypcavbA4GTXcc2KEbF96o4vJiLd66WG2T2jbqfMEg9Rub35DXc7YkyRNXe6q7eE+cZp9zeDFKMNj869fWamB2hiq9W+WLy9PoTtGXvzwxPlHL85brjOfP0Gd7PtOME2fouRnP6U8f/Uk/ev1HOuP5M3T+gPN18+ibqx2X4k1RQVmBnlj5ROg9AADhZmb645l/DD0R9fnez7Vmz5o666pdt+A6SdL2n2xXhwf8QeaJvY4tNS4a7trh1+ra4dfq8lcv1/tfvh/p7kiSar97i4Dgo2SJ8YmqqPQHiQ8dkQ7m96+vl+TPDbzg013adaBEm3YX6tkPtuvV3J3auLtQE/pnamRP/6rf4rKauY7ve329/rBogyQpNcEfQE5J9AdV8/IPr4xet6sgFGiWpN6ZaQ0eU3xcnMorD/chuKI6yVv/lz0xPk4Xjeimhy4+ud52tbl92oka3y9TJwVWdPTr5O/3B1v+fbgfZT7Fx5kuGdVdQ3PaaGB2hi4Z5c9RWDWofmL2saeiqO3rk5roUWGpT77A1yC+jrzYAAAAx4NgPY/cr3OrbX9n2zuSpCm9p9QINk9+frLuePsOlVWUKcWboosHX6yJPSdqWp9pjerD9cOv15m9z9TY7mPrbGNmmj1uduh9MLdzQ6UnpKugtEAf7PhACzYu0IqvVkhqfPA6+KE172BeaEVzXSub63PB4x/qH+tekyQN73RGo/oSTpcOvlQDMweqxFeiYdnD9N2B/tzkk3tN1qRek7R271o9vuLxGseN7DpSktS9TXddc8o1jc61DQBAQ43OGa2zep8Ver9w08Jq+71xXklS3/Z9Q9tSvYef5k9PaFyqUxy7lPjoyd0cNSubg0UyEjwJoZXNUpz+48KhuvVvq0PtXsvdGUo/cce3B2jVl/l6bMkWrdqer0FdMvTs90eF0mk8tHij3tu8TyXlFfo075tqq4QlqUOa/wbtlG7+HLsHS3z6/cJ1KvVVVgs8Xzu2l5ITjm2Vb1V7C0r191U71b19imaN662HF2+S12NHLZBnZrr/gpMafD1Juuq0XrrqtF6h99MGZ+t3C9fr/jfWh7Z9vC1f3x7SWamJ8Xrtpup5dqr2bWI9K6mPdPZJ2Zrz1sZq29ISvTpUWhFa2dyYDwsAAACtRbI3WT8/9ef6z2X/qevm+1f1lFf6U5bdPfFu+Sp92l+8P7Rv/b711Y5P8aZoap+pmtpnaqP7MKbbGC26bNFR29018S7dvfRuSdLQzkMbda3MlEx9VfCVfjD/B5KkaX2n6aU1L8nr8TbqfMEV0S98+oJ6Jxeq3M7U/G3/0PAvr6qWlqQuFSrUAe+LcirV799fpDiXqpGdJzeqL+F0w8gbdMPIG2psH5A5QIuvWKwbFtyg51Y/F/o5kaRKV6m3t76t3u16672r32vJ7gIAoKzULL1x2Rtyzinurjj94q1fKPfrXKUl+BcelleW6zfjf6M7J9wp+60/9pPgSdC0PtP0+ubXjxoHQ9OlJqRWu88844QzmrXYdkNETbC5ahqN34z/jd7f/omSigfqpJw2mnxilhav91eyfuCNDfrjd/03xMlej0b16qBXV+2Ur9JpfL9MSVJmun81xcbdhdq4uzB0jV+88lno9aAuGcpp53/0LBhI/uTLfD2x9IsafSuuJR3HsSgLVMR+8K1N6t7ef6268jSHS6eMpFq3D+pSd6qJRy45Rc+8v1UpDQiw57RL1sDsjGrFANMSPTpU5gtVBveyshkAABznxvccrxfXvKgFGxdIknYV7pLkDyQP7TRU3TK6acHGBSqrKNO/iw8/eda9TXeN6DKiRfv6k9E/qbLIo+Em9Jygv6z5S2iM0/pM09LtSzXlhClHOfLothS/qjbJeXor72PtnP++Pv/h5/W2v+2s/nps+QvKK50nc8H737hQ/ubWZFKvSZq/cX7oZ6jEV6L8En+h8ZM6NW5BCgAAzaFq0Pgva/6i7LRsSVK3jG6hooGXn3S59hXtk5np7ol3a9P+TRrXY1xE+htLTut2ml5e+7IWbFyg/JJ8vbv9XYLNW/ZvkSQVlZq2ft1ZnYueVoe2ierXKV1PX+V/ZOyu+Z/rf5Zv199X5UmSkhI8OrV7O31we/UVC0lej/pkpWnznsJq29ftOug/z/RBumJMz9D2YG7iF5d/Wa19sIBebek4GuqFZf6ih49dNqzJ52qIhPg49e+UXqNo3uVjetR5zLlDu+jcoV0adJ0kr0cLf1z9cc20pHg5588ZbSZ5WNkMAACOc9/u+23tuGVH6P3Vr12tublzlRyfrMknTNaXt/jvN9fvW68Bj/rTbrjfuFrPFW5zps5p0vHn9j9XE3pOUMZ9/uKF5w84X1cMvaI5uqY2iW2U4NkmFUnr9q3Tg8seDKXdS4pP0qndTlXu17m6dMilevqTp1XsLVa/Xp9p7Xqpre97yvc+LTPp5G6Nyx8dSRcOulAXDrow9H7p9qUa/+x4SdLc6XMj1S0AAGr46mdf1dj2/HeeD70e3mW4Nv1oU0t2KWbNHDhTMwfOlCTdtPAmPfrxo/rX9n9pbI+6U6uFS9QEmzf9e6sk6dIn16nCV3uxi0FdMlTqq9TLK/KUkuBR17bJtbaTpCFd29QINgcdWewvp12ykr0ebTqi/dWn9dKctzZqxikNC7zW5pMvv1G7FK+y29Td53AJBpq9HgultDjW4n9NkZ7kf4QyL7847NcCAACIRhN7TtTc3Lnq26Fvte05GTlKT0jXef3Pi1DPmkdaQpq6pndVhatoloJ1U06Yot2Hdqt9cnst2bYktP2WRbfU2n7HwR2hdCCSlOrJUmLJYMk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      "text/plain": [
       "<Figure size 1800x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# same plot but limited to members with extant cards (any time, includes partial cards)\n",
    "ax = plt.gca()\n",
    "borrowing_df.plot(kind='line',x='date',y='books_out',ax=ax, figsize=(25,7),\n",
    "                  title='books out vs. subscription volumes for members with extant cards')\n",
    "borrowing_df.plot(kind='line',x='date',y='card_subscription_volumes', color='green', ax=ax)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 361
    },
    "id": "a581JC56c9he",
    "outputId": "e78bd89d-621b-4dc9-ef43-4b83e4a767f4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-3f774664b86e4dadbe39a6ba05924358\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-3f774664b86e4dadbe39a6ba05924358\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-3f774664b86e4dadbe39a6ba05924358\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"mark\": {\"type\": \"line\", \"stroke\": \"#5276A7\", \"strokeWidth\": 1}, \"encoding\": {\"x\": {\"axis\": {\"title\": \"date\"}, \"field\": \"date\", \"type\": \"temporal\"}, \"y\": {\"axis\": {\"title\": \"books out\", \"titleColor\": \"#5276A7\"}, \"field\": \"books_out\", \"type\": \"quantitative\"}}, \"height\": 275, \"width\": 1200}, {\"mark\": {\"type\": \"line\", \"stroke\": \"green\", \"strokeWidth\": 1}, \"encoding\": {\"x\": {\"axis\": {\"title\": \"Duration of the Lending Library\"}, \"field\": \"date\", \"type\": \"temporal\"}, \"y\": {\"axis\": {\"title\": \"number of volumes\"}, \"field\": \"card_subscription_volumes\", \"type\": \"quantitative\"}}, \"height\": 275, \"title\": \"Books out vs. Subscription volumes for members with extant cards\", \"width\": 1200}], \"data\": {\"name\": \"data-c5a7cadb47d0fbf240baa72012612dfd\"}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-c5a7cadb47d0fbf240baa72012612dfd\": [{\"date\": \"1919-11-17T00:00:00\", \"books_out\": 0, \"subscription_volumes\": 3.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1919-11-18T00:00:00\", \"books_out\": 3, \"subscription_volumes\": 9.0, \"card_subscription_volumes\": 4.0}, {\"date\": \"1919-11-19T00:00:00\", \"books_out\": 5, \"subscription_volumes\": 10.0, \"card_subscription_volumes\": 5.0}, {\"date\": \"1919-11-20T00:00:00\", \"books_out\": 5, \"subscription_volumes\": 11.0, \"card_subscription_volumes\": 5.0}, {\"date\": \"1919-11-21T00:00:00\", \"books_out\": 5, \"subscription_volumes\": 13.0, \"card_subscription_volumes\": 5.0}, {\"date\": \"1919-11-22T00:00:00\", \"books_out\": 4, \"subscription_volumes\": 14.0, \"card_subscription_volumes\": 6.0}, {\"date\": \"1919-11-23T00:00:00\", \"books_out\": 4, \"subscription_volumes\": 14.0, \"card_subscription_volumes\": 6.0}, {\"date\": \"1919-11-24T00:00:00\", \"books_out\": 4, \"subscription_volumes\": 16.0, \"card_subscription_volumes\": 7.0}, {\"date\": \"1919-11-25T00:00:00\", \"books_out\": 5, \"subscription_volumes\": 17.0, \"card_subscription_volumes\": 8.0}, {\"date\": \"1919-11-26T00:00:00\", \"books_out\": 5, \"subscription_volumes\": 19.0, \"card_subscription_volumes\": 8.0}, {\"date\": \"1919-11-27T00:00:00\", \"books_out\": 5, \"subscription_volumes\": 19.0, \"card_subscription_volumes\": 8.0}, {\"date\": \"1919-11-28T00:00:00\", \"books_out\": 5, \"subscription_volumes\": 20.0, \"card_subscription_volumes\": 8.0}, {\"date\": \"1919-11-29T00:00:00\", \"books_out\": 6, \"subscription_volumes\": 27.0, \"card_subscription_volumes\": 9.0}, {\"date\": \"1919-11-30T00:00:00\", \"books_out\": 6, \"subscription_volumes\": 27.0, \"card_subscription_volumes\": 9.0}, {\"date\": \"1919-12-01T00:00:00\", \"books_out\": 8, \"subscription_volumes\": 31.0, \"card_subscription_volumes\": 12.0}, {\"date\": \"1919-12-02T00:00:00\", \"books_out\": 9, \"subscription_volumes\": 34.0, \"card_subscription_volumes\": 13.0}, {\"date\": \"1919-12-03T00:00:00\", \"books_out\": 7, \"subscription_volumes\": 34.0, \"card_subscription_volumes\": 13.0}, {\"date\": \"1919-12-04T00:00:00\", \"books_out\": 8, \"subscription_volumes\": 36.0, \"card_subscription_volumes\": 13.0}, {\"date\": \"1919-12-05T00:00:00\", \"books_out\": 8, \"subscription_volumes\": 36.0, \"card_subscription_volumes\": 13.0}, {\"date\": \"1919-12-06T00:00:00\", \"books_out\": 8, \"subscription_volumes\": 41.0, \"card_subscription_volumes\": 13.0}, {\"date\": \"1919-12-07T00:00:00\", \"books_out\": 8, \"subscription_volumes\": 41.0, \"card_subscription_volumes\": 13.0}, {\"date\": \"1919-12-08T00:00:00\", \"books_out\": 8, \"subscription_volumes\": 42.0, \"card_subscription_volumes\": 13.0}, {\"date\": \"1919-12-09T00:00:00\", \"books_out\": 8, \"subscription_volumes\": 43.0, \"card_subscription_volumes\": 13.0}, {\"date\": \"1919-12-10T00:00:00\", \"books_out\": 8, \"subscription_volumes\": 43.0, \"card_subscription_volumes\": 13.0}, {\"date\": \"1919-12-11T00:00:00\", \"books_out\": 8, \"subscription_volumes\": 46.0, \"card_subscription_volumes\": 14.0}, {\"date\": \"1919-12-12T00:00:00\", \"books_out\": 8, \"subscription_volumes\": 46.0, \"card_subscription_volumes\": 14.0}, {\"date\": \"1919-12-13T00:00:00\", \"books_out\": 9, \"subscription_volumes\": 47.0, \"card_subscription_volumes\": 14.0}, {\"date\": \"1919-12-14T00:00:00\", \"books_out\": 9, \"subscription_volumes\": 47.0, \"card_subscription_volumes\": 14.0}, {\"date\": \"1919-12-15T00:00:00\", \"books_out\": 7, \"subscription_volumes\": 48.0, \"card_subscription_volumes\": 14.0}, {\"date\": \"1919-12-16T00:00:00\", \"books_out\": 7, \"subscription_volumes\": 48.0, \"card_subscription_volumes\": 14.0}, {\"date\": \"1919-12-17T00:00:00\", \"books_out\": 7, \"subscription_volumes\": 48.0, \"card_subscription_volumes\": 13.0}, {\"date\": \"1919-12-18T00:00:00\", \"books_out\": 7, \"subscription_volumes\": 44.0, \"card_subscription_volumes\": 10.0}, {\"date\": \"1919-12-19T00:00:00\", \"books_out\": 8, \"subscription_volumes\": 47.0, \"card_subscription_volumes\": 12.0}, {\"date\": \"1919-12-20T00:00:00\", \"books_out\": 9, \"subscription_volumes\": 47.0, \"card_subscription_volumes\": 12.0}, {\"date\": \"1919-12-21T00:00:00\", \"books_out\": 9, \"subscription_volumes\": 47.0, \"card_subscription_volumes\": 12.0}, {\"date\": \"1919-12-22T00:00:00\", \"books_out\": 9, \"subscription_volumes\": 48.0, \"card_subscription_volumes\": 12.0}, {\"date\": \"1919-12-23T00:00:00\", \"books_out\": 10, \"subscription_volumes\": 49.0, \"card_subscription_volumes\": 13.0}, {\"date\": \"1919-12-24T00:00:00\", \"books_out\": 10, \"subscription_volumes\": 50.0, \"card_subscription_volumes\": 13.0}, {\"date\": \"1919-12-25T00:00:00\", \"books_out\": 10, \"subscription_volumes\": 50.0, \"card_subscription_volumes\": 13.0}, {\"date\": 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\"1920-01-27T00:00:00\", \"books_out\": 9, \"subscription_volumes\": 66.0, \"card_subscription_volumes\": 15.0}, {\"date\": \"1920-01-28T00:00:00\", \"books_out\": 9, \"subscription_volumes\": 68.0, \"card_subscription_volumes\": 15.0}, {\"date\": \"1920-01-29T00:00:00\", \"books_out\": 9, \"subscription_volumes\": 74.0, \"card_subscription_volumes\": 15.0}, {\"date\": \"1920-01-30T00:00:00\", \"books_out\": 9, \"subscription_volumes\": 74.0, \"card_subscription_volumes\": 15.0}, {\"date\": \"1920-01-31T00:00:00\", \"books_out\": 9, \"subscription_volumes\": 74.0, \"card_subscription_volumes\": 15.0}, {\"date\": \"1920-02-01T00:00:00\", \"books_out\": 9, \"subscription_volumes\": 74.0, \"card_subscription_volumes\": 15.0}, {\"date\": \"1920-02-02T00:00:00\", \"books_out\": 9, \"subscription_volumes\": 74.0, \"card_subscription_volumes\": 15.0}, {\"date\": \"1920-02-03T00:00:00\", \"books_out\": 9, \"subscription_volumes\": 72.0, \"card_subscription_volumes\": 15.0}, {\"date\": 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\"1920-02-12T00:00:00\", \"books_out\": 11, \"subscription_volumes\": 73.0, \"card_subscription_volumes\": 15.0}, {\"date\": \"1920-02-13T00:00:00\", \"books_out\": 11, \"subscription_volumes\": 76.0, \"card_subscription_volumes\": 15.0}, {\"date\": \"1920-02-14T00:00:00\", \"books_out\": 11, \"subscription_volumes\": 78.0, \"card_subscription_volumes\": 15.0}, {\"date\": \"1920-02-15T00:00:00\", \"books_out\": 11, \"subscription_volumes\": 78.0, \"card_subscription_volumes\": 15.0}, {\"date\": \"1920-02-16T00:00:00\", \"books_out\": 11, \"subscription_volumes\": 81.0, \"card_subscription_volumes\": 15.0}, {\"date\": \"1920-02-17T00:00:00\", \"books_out\": 11, \"subscription_volumes\": 80.0, \"card_subscription_volumes\": 15.0}, {\"date\": \"1920-02-18T00:00:00\", \"books_out\": 11, \"subscription_volumes\": 80.0, \"card_subscription_volumes\": 15.0}, {\"date\": \"1920-02-19T00:00:00\", \"books_out\": 11, \"subscription_volumes\": 80.0, \"card_subscription_volumes\": 15.0}, {\"date\": 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\"1920-03-23T00:00:00\", \"books_out\": 12, \"subscription_volumes\": 87.0, \"card_subscription_volumes\": 17.0}, {\"date\": \"1920-03-24T00:00:00\", \"books_out\": 10, \"subscription_volumes\": 86.0, \"card_subscription_volumes\": 16.0}, {\"date\": \"1920-03-25T00:00:00\", \"books_out\": 10, \"subscription_volumes\": 87.0, \"card_subscription_volumes\": 16.0}, {\"date\": \"1920-03-26T00:00:00\", \"books_out\": 9, \"subscription_volumes\": 87.0, \"card_subscription_volumes\": 15.0}, {\"date\": \"1920-03-27T00:00:00\", \"books_out\": 9, \"subscription_volumes\": 88.0, \"card_subscription_volumes\": 15.0}, {\"date\": \"1920-03-28T00:00:00\", \"books_out\": 9, \"subscription_volumes\": 86.0, \"card_subscription_volumes\": 15.0}, {\"date\": \"1920-03-29T00:00:00\", \"books_out\": 9, \"subscription_volumes\": 88.0, \"card_subscription_volumes\": 15.0}, {\"date\": \"1920-03-30T00:00:00\", \"books_out\": 9, \"subscription_volumes\": 91.0, \"card_subscription_volumes\": 15.0}, {\"date\": 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\"1942-07-05T00:00:00\", \"books_out\": 21, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-06T00:00:00\", \"books_out\": 21, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-07T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-08T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-09T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-10T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-11T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-12T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-13T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-14T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-15T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-16T00:00:00\", \"books_out\": 20, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-17T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-18T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-19T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-20T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-21T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-22T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-23T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-24T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-25T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-26T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-27T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-28T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-29T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-30T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-07-31T00:00:00\", \"books_out\": 16, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-01T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-02T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-03T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-04T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-05T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-06T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-07T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-08T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-09T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-10T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-11T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-12T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-13T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-14T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-15T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-16T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-17T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-18T00:00:00\", \"books_out\": 15, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-19T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-20T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-21T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-22T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-23T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-24T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-25T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-26T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-27T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-28T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-29T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-30T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-08-31T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-09-01T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}, {\"date\": \"1942-09-02T00:00:00\", \"books_out\": 17, \"subscription_volumes\": 1.0, \"card_subscription_volumes\": 1.0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# altair version of above\n",
    "\n",
    "\n",
    "base = alt.Chart(borrowing_df).encode(\n",
    "    alt.X('date:T', axis=alt.Axis(title='Duration of the Lending Library'))\n",
    ").properties(\n",
    "    width=1200,\n",
    "    height=chart_height,\n",
    "    title='Books out vs. Subscription volumes for members with extant cards'\n",
    ")\n",
    "\n",
    "line_cardsubs_vols = base.mark_line(stroke='green', strokeWidth=1).encode(\n",
    "    alt.Y('card_subscription_volumes',\n",
    "          axis=alt.Axis(title='number of volumes'))\n",
    ")\n",
    "\n",
    "line_books_out + line_cardsubs_vols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6b4uc7aJLT_5"
   },
   "source": [
    "Based on this plot, we have a lot of borrowing going on beyond what the subscriptions indicate should have been allowed. Lots of friends and family borrowing? Overdue books that weren't counted against volume limits?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MePwdwC_xJZx"
   },
   "source": [
    "## percent of available subscription volumes used (members with extant cards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "Xl0Mmq_HxMH1",
    "outputId": "18531967-ad3d-40be-8f5e-597e6d843f45"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>books_out</th>\n",
       "      <th>subscription_volumes</th>\n",
       "      <th>card_subscription_volumes</th>\n",
       "      <th>pct_used</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1919-11-17</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1919-11-18</td>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1919-11-19</td>\n",
       "      <td>5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1919-11-20</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1919-11-21</td>\n",
       "      <td>5</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>8321</th>\n",
       "      <td>1942-08-29</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.00</td>\n",
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       "    <tr>\n",
       "      <th>8322</th>\n",
       "      <td>1942-08-30</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8323</th>\n",
       "      <td>1942-08-31</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.00</td>\n",
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       "    <tr>\n",
       "      <th>8324</th>\n",
       "      <td>1942-09-01</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8325</th>\n",
       "      <td>1942-09-02</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
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       "<p>8326 rows × 5 columns</p>\n",
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      "text/plain": [
       "           date  books_out  subscription_volumes  card_subscription_volumes  \\\n",
       "0    1919-11-17          0                   3.0                        1.0   \n",
       "1    1919-11-18          3                   9.0                        4.0   \n",
       "2    1919-11-19          5                  10.0                        5.0   \n",
       "3    1919-11-20          5                  11.0                        5.0   \n",
       "4    1919-11-21          5                  13.0                        5.0   \n",
       "...         ...        ...                   ...                        ...   \n",
       "8321 1942-08-29         17                   1.0                        1.0   \n",
       "8322 1942-08-30         17                   1.0                        1.0   \n",
       "8323 1942-08-31         17                   1.0                        1.0   \n",
       "8324 1942-09-01         17                   1.0                        1.0   \n",
       "8325 1942-09-02         17                   1.0                        1.0   \n",
       "\n",
       "      pct_used  \n",
       "0         0.00  \n",
       "1         0.75  \n",
       "2         1.00  \n",
       "3         1.00  \n",
       "4         1.00  \n",
       "...        ...  \n",
       "8321     17.00  \n",
       "8322     17.00  \n",
       "8323     17.00  \n",
       "8324     17.00  \n",
       "8325     17.00  \n",
       "\n",
       "[8326 rows x 5 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate what percent of available subscription volumes was used (members with cards only)\n",
    "borrowing_df['pct_used'] = borrowing_df.apply(lambda x: x.books_out / x.card_subscription_volumes, axis=1)\n",
    "borrowing_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Azy8K6zpxqQS",
    "outputId": "f02da003-0afa-4af2-aa6e-e64f55e383d2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "min      0.000000\n",
       "max     26.000000\n",
       "mean     1.703751\n",
       "Name: pct_used, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "borrowing_df.pct_used.agg(['min', 'max', 'mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 284
    },
    "id": "EtjRfxynxxjW",
    "outputId": "e1b5df80-e38f-4c2d-fda2-dd405f533f2b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f13c207f820>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "borrowing_df.pct_used.hist(bins=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SYSi6IE1v33M"
   },
   "source": [
    "# limit to borrows and subscriptions that overlap\n",
    "\n",
    "To calculate better based on partial borrowing records and borrowing without subscriptions, only consider subscriptions with at least one borrow and borrows that overlap with at least one subscription."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NIyhv_2dZN6F"
   },
   "source": [
    "## identify borrows within subscriptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tHLKDfmfLedP",
    "outputId": "03bb279f-e0fc-4eb3-815e-af6df9cd0174"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    15622\n",
       "0     2343\n",
       "2     1737\n",
       "3       83\n",
       "4        3\n",
       "6        1\n",
       "Name: within_subscription, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# identify borrows that occur with/without active subscriptions\n",
    "# for each borrow event, check if there is a subscription event for the same first member uri \n",
    "# with any overlap in dates\n",
    "\n",
    "# make a copy of the dataframe so pandas won't complain about setting values on a slice\n",
    "borrow_events_df = borrow_events.copy()\n",
    "# split out multiple member uris to match on member_subs df\n",
    "borrow_events_df[['first_member_uri','second_member_uri']] = borrow_events_df.member_uris.str.split(';', expand=True)\n",
    "\n",
    "def borrow_within_sub(borrow):\n",
    "  return len(member_subs[(member_subs.first_member_uri == borrow.first_member_uri) & \n",
    "              (member_subs.start_date <= borrow.end_date) &\n",
    "              (member_subs.end_date >= borrow.start_date)])\n",
    "\n",
    "borrow_events_df['within_subscription'] = borrow_events_df.apply(borrow_within_sub, axis=1)\n",
    "borrow_events_df.within_subscription.value_counts()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 939
    },
    "id": "sKxTWdqyapGv",
    "outputId": "e4b24a0e-e343-407d-d716-b8fdfb739c66"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-b8a682f2-e8d9-45dd-bd76-49d439957a69\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_type</th>\n",
       "      <th>start_date</th>\n",
       "      <th>end_date</th>\n",
       "      <th>member_uris</th>\n",
       "      <th>member_names</th>\n",
       "      <th>member_sort_names</th>\n",
       "      <th>subscription_price_paid</th>\n",
       "      <th>subscription_deposit</th>\n",
       "      <th>subscription_duration</th>\n",
       "      <th>subscription_duration_days</th>\n",
       "      <th>...</th>\n",
       "      <th>item_notes</th>\n",
       "      <th>source_type</th>\n",
       "      <th>source_citation</th>\n",
       "      <th>source_manifest</th>\n",
       "      <th>source_image</th>\n",
       "      <th>start_datetime</th>\n",
       "      <th>end_datetime</th>\n",
       "      <th>first_member_uri</th>\n",
       "      <th>second_member_uri</th>\n",
       "      <th>within_subscription</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>Borrow</td>\n",
       "      <td>1920-01-12</td>\n",
       "      <td>1920-01-22</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Blanche Reverchon</td>\n",
       "      <td>Reverchon, Blanche</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lending Library Card</td>\n",
       "      <td>Sylvia Beach, Blanche Reverchon Lending Librar...</td>\n",
       "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
       "      <td>https://iiif.princeton.edu/loris/figgy_prod/7e...</td>\n",
       "      <td>1920-01-12</td>\n",
       "      <td>1920-01-22</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>Borrow</td>\n",
       "      <td>1920-01-22</td>\n",
       "      <td>1920-03-13</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Blanche Reverchon</td>\n",
       "      <td>Reverchon, Blanche</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lending Library Card</td>\n",
       "      <td>Sylvia Beach, Blanche Reverchon Lending Librar...</td>\n",
       "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
       "      <td>https://iiif.princeton.edu/loris/figgy_prod/7e...</td>\n",
       "      <td>1920-01-22</td>\n",
       "      <td>1920-03-13</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>Borrow</td>\n",
       "      <td>1920-03-13</td>\n",
       "      <td>1920-04-17</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Blanche Reverchon</td>\n",
       "      <td>Reverchon, Blanche</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lending Library Card</td>\n",
       "      <td>Sylvia Beach, Blanche Reverchon Lending Librar...</td>\n",
       "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
       "      <td>https://iiif.princeton.edu/loris/figgy_prod/7e...</td>\n",
       "      <td>1920-03-13</td>\n",
       "      <td>1920-04-17</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>Borrow</td>\n",
       "      <td>1920-04-13</td>\n",
       "      <td>1920-04-19</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>André Maurois</td>\n",
       "      <td>Maurois, André</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lending Library Card</td>\n",
       "      <td>Sylvia Beach, André Maurois Lending Library Ca...</td>\n",
       "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
       "      <td>https://iiif.princeton.edu/loris/figgy_prod/65...</td>\n",
       "      <td>1920-04-13</td>\n",
       "      <td>1920-04-19</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>Borrow</td>\n",
       "      <td>1920-04-13</td>\n",
       "      <td>1921-04-22</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>André Maurois</td>\n",
       "      <td>Maurois, André</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lending Library Card</td>\n",
       "      <td>Sylvia Beach, André Maurois Lending Library Ca...</td>\n",
       "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
       "      <td>https://iiif.princeton.edu/loris/figgy_prod/65...</td>\n",
       "      <td>1920-04-13</td>\n",
       "      <td>1921-04-22</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29903</th>\n",
       "      <td>Borrow</td>\n",
       "      <td>1961-06-30</td>\n",
       "      <td>1961-10-04</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Henri Michaux</td>\n",
       "      <td>Michaux, Henri</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>At least two sets of De Quincey's works circul...</td>\n",
       "      <td>Lending Library Card</td>\n",
       "      <td>Sylvia Beach, Henri Michaux Lending Library Ca...</td>\n",
       "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
       "      <td>https://iiif.princeton.edu/loris/figgy_prod/5b...</td>\n",
       "      <td>1961-06-30</td>\n",
       "      <td>1961-10-04</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29904</th>\n",
       "      <td>Borrow</td>\n",
       "      <td>1961-06-30</td>\n",
       "      <td>1961-10-04</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Henri Michaux</td>\n",
       "      <td>Michaux, Henri</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>At least two sets of De Quincey's works circul...</td>\n",
       "      <td>Lending Library Card</td>\n",
       "      <td>Sylvia Beach, Henri Michaux Lending Library Ca...</td>\n",
       "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
       "      <td>https://iiif.princeton.edu/loris/figgy_prod/5b...</td>\n",
       "      <td>1961-06-30</td>\n",
       "      <td>1961-10-04</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29905</th>\n",
       "      <td>Borrow</td>\n",
       "      <td>1961-06-30</td>\n",
       "      <td>1961-10-04</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Henri Michaux</td>\n",
       "      <td>Michaux, Henri</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>At least two sets of De Quincey's works circul...</td>\n",
       "      <td>Lending Library Card</td>\n",
       "      <td>Sylvia Beach, Henri Michaux Lending Library Ca...</td>\n",
       "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
       "      <td>https://iiif.princeton.edu/loris/figgy_prod/5b...</td>\n",
       "      <td>1961-06-30</td>\n",
       "      <td>1961-10-04</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29908</th>\n",
       "      <td>Borrow</td>\n",
       "      <td>1961-10-04</td>\n",
       "      <td>1962-03-21</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Ann Samyn</td>\n",
       "      <td>Samyn, Ann</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lending Library Card</td>\n",
       "      <td>Sylvia Beach, Mademoiselle Anne Samyn Lending ...</td>\n",
       "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
       "      <td>https://iiif.princeton.edu/loris/figgy_prod/9a...</td>\n",
       "      <td>1961-10-04</td>\n",
       "      <td>1962-03-21</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29907</th>\n",
       "      <td>Borrow</td>\n",
       "      <td>1961-10-04</td>\n",
       "      <td>1962-03-21</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Ann Samyn</td>\n",
       "      <td>Samyn, Ann</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lending Library Card</td>\n",
       "      <td>Sylvia Beach, Mademoiselle Anne Samyn Lending ...</td>\n",
       "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
       "      <td>https://iiif.princeton.edu/loris/figgy_prod/9a...</td>\n",
       "      <td>1961-10-04</td>\n",
       "      <td>1962-03-21</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2343 rows × 33 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b8a682f2-e8d9-45dd-bd76-49d439957a69')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
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       "      \n",
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       "      gap: 12px;\n",
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       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
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       "          const element = document.querySelector('#df-b8a682f2-e8d9-45dd-bd76-49d439957a69');\n",
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
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       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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       "  "
      ],
      "text/plain": [
       "      event_type  start_date    end_date  \\\n",
       "768       Borrow  1920-01-12  1920-01-22   \n",
       "779       Borrow  1920-01-22  1920-03-13   \n",
       "830       Borrow  1920-03-13  1920-04-17   \n",
       "865       Borrow  1920-04-13  1920-04-19   \n",
       "863       Borrow  1920-04-13  1921-04-22   \n",
       "...          ...         ...         ...   \n",
       "29903     Borrow  1961-06-30  1961-10-04   \n",
       "29904     Borrow  1961-06-30  1961-10-04   \n",
       "29905     Borrow  1961-06-30  1961-10-04   \n",
       "29908     Borrow  1961-10-04  1962-03-21   \n",
       "29907     Borrow  1961-10-04  1962-03-21   \n",
       "\n",
       "                                             member_uris       member_names  \\\n",
       "768    https://shakespeareandco.princeton.edu/members...  Blanche Reverchon   \n",
       "779    https://shakespeareandco.princeton.edu/members...  Blanche Reverchon   \n",
       "830    https://shakespeareandco.princeton.edu/members...  Blanche Reverchon   \n",
       "865    https://shakespeareandco.princeton.edu/members...      André Maurois   \n",
       "863    https://shakespeareandco.princeton.edu/members...      André Maurois   \n",
       "...                                                  ...                ...   \n",
       "29903  https://shakespeareandco.princeton.edu/members...      Henri Michaux   \n",
       "29904  https://shakespeareandco.princeton.edu/members...      Henri Michaux   \n",
       "29905  https://shakespeareandco.princeton.edu/members...      Henri Michaux   \n",
       "29908  https://shakespeareandco.princeton.edu/members...          Ann Samyn   \n",
       "29907  https://shakespeareandco.princeton.edu/members...          Ann Samyn   \n",
       "\n",
       "        member_sort_names  subscription_price_paid  subscription_deposit  \\\n",
       "768    Reverchon, Blanche                      NaN                   NaN   \n",
       "779    Reverchon, Blanche                      NaN                   NaN   \n",
       "830    Reverchon, Blanche                      NaN                   NaN   \n",
       "865        Maurois, André                      NaN                   NaN   \n",
       "863        Maurois, André                      NaN                   NaN   \n",
       "...                   ...                      ...                   ...   \n",
       "29903      Michaux, Henri                      NaN                   NaN   \n",
       "29904      Michaux, Henri                      NaN                   NaN   \n",
       "29905      Michaux, Henri                      NaN                   NaN   \n",
       "29908          Samyn, Ann                      NaN                   NaN   \n",
       "29907          Samyn, Ann                      NaN                   NaN   \n",
       "\n",
       "      subscription_duration  subscription_duration_days  ...  \\\n",
       "768                     NaN                         NaN  ...   \n",
       "779                     NaN                         NaN  ...   \n",
       "830                     NaN                         NaN  ...   \n",
       "865                     NaN                         NaN  ...   \n",
       "863                     NaN                         NaN  ...   \n",
       "...                     ...                         ...  ...   \n",
       "29903                   NaN                         NaN  ...   \n",
       "29904                   NaN                         NaN  ...   \n",
       "29905                   NaN                         NaN  ...   \n",
       "29908                   NaN                         NaN  ...   \n",
       "29907                   NaN                         NaN  ...   \n",
       "\n",
       "                                              item_notes  \\\n",
       "768                                                  NaN   \n",
       "779                                                  NaN   \n",
       "830                                                  NaN   \n",
       "865                                                  NaN   \n",
       "863                                                  NaN   \n",
       "...                                                  ...   \n",
       "29903  At least two sets of De Quincey's works circul...   \n",
       "29904  At least two sets of De Quincey's works circul...   \n",
       "29905  At least two sets of De Quincey's works circul...   \n",
       "29908                                                NaN   \n",
       "29907                                                NaN   \n",
       "\n",
       "                source_type  \\\n",
       "768    Lending Library Card   \n",
       "779    Lending Library Card   \n",
       "830    Lending Library Card   \n",
       "865    Lending Library Card   \n",
       "863    Lending Library Card   \n",
       "...                     ...   \n",
       "29903  Lending Library Card   \n",
       "29904  Lending Library Card   \n",
       "29905  Lending Library Card   \n",
       "29908  Lending Library Card   \n",
       "29907  Lending Library Card   \n",
       "\n",
       "                                         source_citation  \\\n",
       "768    Sylvia Beach, Blanche Reverchon Lending Librar...   \n",
       "779    Sylvia Beach, Blanche Reverchon Lending Librar...   \n",
       "830    Sylvia Beach, Blanche Reverchon Lending Librar...   \n",
       "865    Sylvia Beach, André Maurois Lending Library Ca...   \n",
       "863    Sylvia Beach, André Maurois Lending Library Ca...   \n",
       "...                                                  ...   \n",
       "29903  Sylvia Beach, Henri Michaux Lending Library Ca...   \n",
       "29904  Sylvia Beach, Henri Michaux Lending Library Ca...   \n",
       "29905  Sylvia Beach, Henri Michaux Lending Library Ca...   \n",
       "29908  Sylvia Beach, Mademoiselle Anne Samyn Lending ...   \n",
       "29907  Sylvia Beach, Mademoiselle Anne Samyn Lending ...   \n",
       "\n",
       "                                         source_manifest  \\\n",
       "768    https://figgy.princeton.edu/concern/scanned_re...   \n",
       "779    https://figgy.princeton.edu/concern/scanned_re...   \n",
       "830    https://figgy.princeton.edu/concern/scanned_re...   \n",
       "865    https://figgy.princeton.edu/concern/scanned_re...   \n",
       "863    https://figgy.princeton.edu/concern/scanned_re...   \n",
       "...                                                  ...   \n",
       "29903  https://figgy.princeton.edu/concern/scanned_re...   \n",
       "29904  https://figgy.princeton.edu/concern/scanned_re...   \n",
       "29905  https://figgy.princeton.edu/concern/scanned_re...   \n",
       "29908  https://figgy.princeton.edu/concern/scanned_re...   \n",
       "29907  https://figgy.princeton.edu/concern/scanned_re...   \n",
       "\n",
       "                                            source_image  start_datetime  \\\n",
       "768    https://iiif.princeton.edu/loris/figgy_prod/7e...      1920-01-12   \n",
       "779    https://iiif.princeton.edu/loris/figgy_prod/7e...      1920-01-22   \n",
       "830    https://iiif.princeton.edu/loris/figgy_prod/7e...      1920-03-13   \n",
       "865    https://iiif.princeton.edu/loris/figgy_prod/65...      1920-04-13   \n",
       "863    https://iiif.princeton.edu/loris/figgy_prod/65...      1920-04-13   \n",
       "...                                                  ...             ...   \n",
       "29903  https://iiif.princeton.edu/loris/figgy_prod/5b...      1961-06-30   \n",
       "29904  https://iiif.princeton.edu/loris/figgy_prod/5b...      1961-06-30   \n",
       "29905  https://iiif.princeton.edu/loris/figgy_prod/5b...      1961-06-30   \n",
       "29908  https://iiif.princeton.edu/loris/figgy_prod/9a...      1961-10-04   \n",
       "29907  https://iiif.princeton.edu/loris/figgy_prod/9a...      1961-10-04   \n",
       "\n",
       "       end_datetime                                   first_member_uri  \\\n",
       "768      1920-01-22  https://shakespeareandco.princeton.edu/members...   \n",
       "779      1920-03-13  https://shakespeareandco.princeton.edu/members...   \n",
       "830      1920-04-17  https://shakespeareandco.princeton.edu/members...   \n",
       "865      1920-04-19  https://shakespeareandco.princeton.edu/members...   \n",
       "863      1921-04-22  https://shakespeareandco.princeton.edu/members...   \n",
       "...             ...                                                ...   \n",
       "29903    1961-10-04  https://shakespeareandco.princeton.edu/members...   \n",
       "29904    1961-10-04  https://shakespeareandco.princeton.edu/members...   \n",
       "29905    1961-10-04  https://shakespeareandco.princeton.edu/members...   \n",
       "29908    1962-03-21  https://shakespeareandco.princeton.edu/members...   \n",
       "29907    1962-03-21  https://shakespeareandco.princeton.edu/members...   \n",
       "\n",
       "      second_member_uri within_subscription  \n",
       "768                None                   0  \n",
       "779                None                   0  \n",
       "830                None                   0  \n",
       "865                None                   0  \n",
       "863                None                   0  \n",
       "...                 ...                 ...  \n",
       "29903              None                   0  \n",
       "29904              None                   0  \n",
       "29905              None                   0  \n",
       "29908              None                   0  \n",
       "29907              None                   0  \n",
       "\n",
       "[2343 rows x 33 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect results\n",
    "# borrows without subscription\n",
    "borrow_events_df[borrow_events_df.within_subscription == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xqbR6tiIcMI8",
    "outputId": "c4be9b52-25b5-4ef2-9cc6-608815ae6ad8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mlle Ottocar                               104\n",
       "François Valéry                            101\n",
       "Bravig Imbs                                 76\n",
       "Jeanne Fournier                             73\n",
       "Dorothy Plummer                             73\n",
       "                                          ... \n",
       "M. Villars                                   1\n",
       "Mary Sophie Ludlow                           1\n",
       "Andrée (Hirsch) Pelorson / Mme Pelorson      1\n",
       "Max Christian Wegner                         1\n",
       "Nathalie Sarraute                            1\n",
       "Name: member_names, Length: 230, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "borrow_events_df[borrow_events_df.within_subscription == 0].member_names.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
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    "preliminary inspection of borrows occurring outside of subscriptions \n",
    "\n",
    "- Blanch Reverchon: subs Dec 1919 - Jan 1920, but borrows continue into March and April\n",
    "- André Maurois: 3month sub Dec 1919 - March 1920; borrowing continues into March and April\n",
    "- Henri Michaux: no documented subscriptions; borrow events 1944; 1950 – 1951; 1955; 1961 (all after bookshop officiall closed)\n",
    "- Mlle Ottocar: subs 1921-1927, partial in 1929; borrow events 1920-1931\n",
    "- François Valéry: no documented subscriptions; most likely Beach let him borrow without one\n",
    "- Jeanne Fournier: subs 1925-1930; borrows 1925-1931 (likely missing subs due to missing 1930s logbooks)\n",
    "- Bravig Imbs: subs 1925, 1928; borrows 1927-1931\n",
    "- Dorothy Plummer: only one 1m sub in 1936; borrow events 1931-1937\n"
   ]
  },
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       "      <th>1135</th>\n",
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       "      <td>1920-12-04</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1920-12-04</td>\n",
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       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
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       "      <th>1143</th>\n",
       "      <td>Borrow</td>\n",
       "      <td>1920-12-09</td>\n",
       "      <td>1920-12-13</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Mlle Tournier</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
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       "      <td>1920-12-09</td>\n",
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       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Mlle Tournier</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1920-12-09</td>\n",
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       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
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       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Miss Somerville and Miss Somerville / The Miss...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Sylvia Beach, The Misses Somerville Lending Li...</td>\n",
       "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
       "      <td>https://iiif.princeton.edu/loris/figgy_prod/a7...</td>\n",
       "      <td>1923-01-10</td>\n",
       "      <td>1923-02-24</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
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       "      <td>Borrow</td>\n",
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       "      <td>1941-03-24</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Mrs. Emory Whitridge</td>\n",
       "      <td>Whitridge, Mrs. Emory</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lending Library Card</td>\n",
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       "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
       "      <td>https://iiif.princeton.edu/loris/figgy_prod/23...</td>\n",
       "      <td>1941-03-18</td>\n",
       "      <td>1941-03-24</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
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       "      <th>28285</th>\n",
       "      <td>Borrow</td>\n",
       "      <td>1941-04-26</td>\n",
       "      <td>1941-05-29</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Fred Davaine</td>\n",
       "      <td>Davaine, Fred</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lending Library Card</td>\n",
       "      <td>Sylvia Beach, Fred Davaine Lending Library Car...</td>\n",
       "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
       "      <td>https://iiif.princeton.edu/loris/figgy_prod/43...</td>\n",
       "      <td>1941-04-26</td>\n",
       "      <td>1941-05-29</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
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       "      <td>1941-05-29</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
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       "      <td>Schueller, Mme</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lending Library Card</td>\n",
       "      <td>Sylvia Beach, Juanita Schueller Lending Librar...</td>\n",
       "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
       "      <td>https://iiif.princeton.edu/loris/figgy_prod/3f...</td>\n",
       "      <td>1941-04-28</td>\n",
       "      <td>1941-05-29</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>None</td>\n",
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       "      <td>Borrow</td>\n",
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       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Mme Schueller</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lending Library Card</td>\n",
       "      <td>Sylvia Beach, Juanita Schueller Lending Librar...</td>\n",
       "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
       "      <td>https://iiif.princeton.edu/loris/figgy_prod/3f...</td>\n",
       "      <td>1941-04-28</td>\n",
       "      <td>1941-05-29</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>None</td>\n",
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       "      <th>29069</th>\n",
       "      <td>Borrow</td>\n",
       "      <td>1941-10-15</td>\n",
       "      <td>1941-12-01</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Jacques Lacan</td>\n",
       "      <td>Lacan, Jacques</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lending Library Card</td>\n",
       "      <td>Sylvia Beach, Jacques Lacan Lending Library Ca...</td>\n",
       "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
       "      <td>https://iiif.princeton.edu/loris/figgy_prod/4c...</td>\n",
       "      <td>1941-10-15</td>\n",
       "      <td>1941-12-01</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
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      "text/plain": [
       "      event_type  start_date    end_date  \\\n",
       "1135      Borrow  1920-12-04  1920-12-09   \n",
       "1139      Borrow  1920-12-04  1920-12-09   \n",
       "1143      Borrow  1920-12-09  1920-12-13   \n",
       "1146      Borrow  1920-12-09  1920-12-13   \n",
       "2680      Borrow  1923-01-10  1923-02-24   \n",
       "...          ...         ...         ...   \n",
       "28070     Borrow  1941-03-18  1941-03-24   \n",
       "28285     Borrow  1941-04-26  1941-05-29   \n",
       "28297     Borrow  1941-04-28  1941-05-29   \n",
       "28296     Borrow  1941-04-28  1941-05-29   \n",
       "29069     Borrow  1941-10-15  1941-12-01   \n",
       "\n",
       "                                             member_uris  \\\n",
       "1135   https://shakespeareandco.princeton.edu/members...   \n",
       "1139   https://shakespeareandco.princeton.edu/members...   \n",
       "1143   https://shakespeareandco.princeton.edu/members...   \n",
       "1146   https://shakespeareandco.princeton.edu/members...   \n",
       "2680   https://shakespeareandco.princeton.edu/members...   \n",
       "...                                                  ...   \n",
       "28070  https://shakespeareandco.princeton.edu/members...   \n",
       "28285  https://shakespeareandco.princeton.edu/members...   \n",
       "28297  https://shakespeareandco.princeton.edu/members...   \n",
       "28296  https://shakespeareandco.princeton.edu/members...   \n",
       "29069  https://shakespeareandco.princeton.edu/members...   \n",
       "\n",
       "                                            member_names  \\\n",
       "1135                                       Mlle Tournier   \n",
       "1139                                       Mlle Tournier   \n",
       "1143                                       Mlle Tournier   \n",
       "1146                                       Mlle Tournier   \n",
       "2680   Miss Somerville and Miss Somerville / The Miss...   \n",
       "...                                                  ...   \n",
       "28070                               Mrs. Emory Whitridge   \n",
       "28285                                       Fred Davaine   \n",
       "28297                                      Mme Schueller   \n",
       "28296                                      Mme Schueller   \n",
       "29069                                      Jacques Lacan   \n",
       "\n",
       "            member_sort_names  subscription_price_paid  subscription_deposit  \\\n",
       "1135           Tournier, Mlle                      NaN                   NaN   \n",
       "1139           Tournier, Mlle                      NaN                   NaN   \n",
       "1143           Tournier, Mlle                      NaN                   NaN   \n",
       "1146           Tournier, Mlle                      NaN                   NaN   \n",
       "2680   Somerville, The Misses                      NaN                   NaN   \n",
       "...                       ...                      ...                   ...   \n",
       "28070   Whitridge, Mrs. Emory                      NaN                   NaN   \n",
       "28285           Davaine, Fred                      NaN                   NaN   \n",
       "28297          Schueller, Mme                      NaN                   NaN   \n",
       "28296          Schueller, Mme                      NaN                   NaN   \n",
       "29069          Lacan, Jacques                      NaN                   NaN   \n",
       "\n",
       "      subscription_duration  subscription_duration_days  ...  item_notes  \\\n",
       "1135                    NaN                         NaN  ...         NaN   \n",
       "1139                    NaN                         NaN  ...         NaN   \n",
       "1143                    NaN                         NaN  ...         NaN   \n",
       "1146                    NaN                         NaN  ...         NaN   \n",
       "2680                    NaN                         NaN  ...         NaN   \n",
       "...                     ...                         ...  ...         ...   \n",
       "28070                   NaN                         NaN  ...         NaN   \n",
       "28285                   NaN                         NaN  ...         NaN   \n",
       "28297                   NaN                         NaN  ...         NaN   \n",
       "28296                   NaN                         NaN  ...         NaN   \n",
       "29069                   NaN                         NaN  ...         NaN   \n",
       "\n",
       "                source_type  \\\n",
       "1135   Lending Library Card   \n",
       "1139   Lending Library Card   \n",
       "1143   Lending Library Card   \n",
       "1146   Lending Library Card   \n",
       "2680   Lending Library Card   \n",
       "...                     ...   \n",
       "28070  Lending Library Card   \n",
       "28285  Lending Library Card   \n",
       "28297  Lending Library Card   \n",
       "28296  Lending Library Card   \n",
       "29069  Lending Library Card   \n",
       "\n",
       "                                         source_citation  \\\n",
       "1135   Sylvia Beach, Mlle Tournier Lending Library Ca...   \n",
       "1139   Sylvia Beach, Mlle Tournier Lending Library Ca...   \n",
       "1143   Sylvia Beach, Mlle Tournier Lending Library Ca...   \n",
       "1146   Sylvia Beach, Mlle Tournier Lending Library Ca...   \n",
       "2680   Sylvia Beach, The Misses Somerville Lending Li...   \n",
       "...                                                  ...   \n",
       "28070  Sylvia Beach, Mrs. Emory Whitridge Lending Lib...   \n",
       "28285  Sylvia Beach, Fred Davaine Lending Library Car...   \n",
       "28297  Sylvia Beach, Juanita Schueller Lending Librar...   \n",
       "28296  Sylvia Beach, Juanita Schueller Lending Librar...   \n",
       "29069  Sylvia Beach, Jacques Lacan Lending Library Ca...   \n",
       "\n",
       "                                         source_manifest  \\\n",
       "1135   https://figgy.princeton.edu/concern/scanned_re...   \n",
       "1139   https://figgy.princeton.edu/concern/scanned_re...   \n",
       "1143   https://figgy.princeton.edu/concern/scanned_re...   \n",
       "1146   https://figgy.princeton.edu/concern/scanned_re...   \n",
       "2680   https://figgy.princeton.edu/concern/scanned_re...   \n",
       "...                                                  ...   \n",
       "28070  https://figgy.princeton.edu/concern/scanned_re...   \n",
       "28285  https://figgy.princeton.edu/concern/scanned_re...   \n",
       "28297  https://figgy.princeton.edu/concern/scanned_re...   \n",
       "28296  https://figgy.princeton.edu/concern/scanned_re...   \n",
       "29069  https://figgy.princeton.edu/concern/scanned_re...   \n",
       "\n",
       "                                            source_image  start_datetime  \\\n",
       "1135   https://iiif.princeton.edu/loris/figgy_prod/bd...      1920-12-04   \n",
       "1139   https://iiif.princeton.edu/loris/figgy_prod/bd...      1920-12-04   \n",
       "1143   https://iiif.princeton.edu/loris/figgy_prod/bd...      1920-12-09   \n",
       "1146   https://iiif.princeton.edu/loris/figgy_prod/bd...      1920-12-09   \n",
       "2680   https://iiif.princeton.edu/loris/figgy_prod/a7...      1923-01-10   \n",
       "...                                                  ...             ...   \n",
       "28070  https://iiif.princeton.edu/loris/figgy_prod/23...      1941-03-18   \n",
       "28285  https://iiif.princeton.edu/loris/figgy_prod/43...      1941-04-26   \n",
       "28297  https://iiif.princeton.edu/loris/figgy_prod/3f...      1941-04-28   \n",
       "28296  https://iiif.princeton.edu/loris/figgy_prod/3f...      1941-04-28   \n",
       "29069  https://iiif.princeton.edu/loris/figgy_prod/4c...      1941-10-15   \n",
       "\n",
       "       end_datetime                                   first_member_uri  \\\n",
       "1135     1920-12-09  https://shakespeareandco.princeton.edu/members...   \n",
       "1139     1920-12-09  https://shakespeareandco.princeton.edu/members...   \n",
       "1143     1920-12-13  https://shakespeareandco.princeton.edu/members...   \n",
       "1146     1920-12-13  https://shakespeareandco.princeton.edu/members...   \n",
       "2680     1923-02-24  https://shakespeareandco.princeton.edu/members...   \n",
       "...             ...                                                ...   \n",
       "28070    1941-03-24  https://shakespeareandco.princeton.edu/members...   \n",
       "28285    1941-05-29  https://shakespeareandco.princeton.edu/members...   \n",
       "28297    1941-05-29  https://shakespeareandco.princeton.edu/members...   \n",
       "28296    1941-05-29  https://shakespeareandco.princeton.edu/members...   \n",
       "29069    1941-12-01  https://shakespeareandco.princeton.edu/members...   \n",
       "\n",
       "      second_member_uri within_subscription  \n",
       "1135               None                   3  \n",
       "1139               None                   3  \n",
       "1143               None                   3  \n",
       "1146               None                   3  \n",
       "2680               None                   3  \n",
       "...                 ...                 ...  \n",
       "28070              None                   3  \n",
       "28285              None                   3  \n",
       "28297              None                   3  \n",
       "28296              None                   3  \n",
       "29069              None                   3  \n",
       "\n",
       "[87 rows x 33 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# borrows overlapping with multiple subscriptions\n",
    "borrow_events_df[borrow_events_df.within_subscription > 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0uiHkRhoeQyH",
    "outputId": "4414f56d-c15b-48d9-a851-123ca69051be"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Leo Stein                                                     11\n",
       "Stanislas Pascal Franchot                                      6\n",
       "Mlle Tournier                                                  4\n",
       "Raymonde Lamirault                                             4\n",
       "Renée Antoine-May                                              4\n",
       "Mrs. Jackson                                                   4\n",
       "Eda Lord                                                       4\n",
       "Mrs. Thornton Baker                                            3\n",
       "Anatole Rivoallan                                              3\n",
       "Fanny Rirachowsky                                              2\n",
       "Mme Schueller                                                  2\n",
       "Mrs. Emory Whitridge                                           2\n",
       "Eric Culley                                                    2\n",
       "Louise Olga Bouniols                                           2\n",
       "Adelaide W. Massey / A. W. Massey                              2\n",
       "E. M. Churchill                                                2\n",
       "Gwen Rowe-Dutton                                               2\n",
       "Troise Proix                                                   2\n",
       "Madeleine Rolland                                              2\n",
       "Steele Savage                                                  2\n",
       "Mlle Ottocar                                                   2\n",
       "Mlle Valerio                                                   2\n",
       "Marthe Lamy                                                    1\n",
       "Fred Davaine                                                   1\n",
       "Bianca (Bienenfeld) Lamblin / Mlle Bianca Bienenfeld           1\n",
       "Madeleine Blaess                                               1\n",
       "Hélène de Wendel / Comtesse de Noailles                        1\n",
       "Mary Sophie Ludlow                                             1\n",
       "Brigit Patmore                                                 1\n",
       "Pierre Denoel                                                  1\n",
       "Eric Delimal                                                   1\n",
       "André de la Forest-Divonne                                     1\n",
       "Thomas Edwards                                                 1\n",
       "Paul Gerbault                                                  1\n",
       "Norma Faulkner                                                 1\n",
       "Countess de Vogué                                              1\n",
       "Ada (Hitchcock) MacLeish;Archibald MacLeish                    1\n",
       "Miss Somerville and Miss Somerville / The Misses Somervile     1\n",
       "Henry W. Wibbelz                                               1\n",
       "Jacques Lacan                                                  1\n",
       "Name: member_names, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "borrow_events_df[borrow_events_df.within_subscription > 2].member_names.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0eTPjQ3seVX_"
   },
   "source": [
    "preliminary inspection of borrows occurring overlapping with > 2 subscriptions \n",
    "\n",
    "Would expect to see this for borrows that start under one subscription and end in another AND for members who purchased a supplement\n",
    "\n",
    "- Leo Stein: supplement apr 8 - may 8 1924; 3 books borrowed May 7 would overlap with march 2 renewal, apr 8 supplement, may 18 renewal? (possible data error here: 2m 15 day renewal may 13-jul 28 overlaps with june 2 renewal)\n",
    "- Franchot: 2 overlapping supplements\n",
    "\n",
    "Some of these have odd-looking subscriptions, could be data errors in subscription dates (Eda Lord, Rivoallan), but calculation seems to be reasonable based on the data we have now"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "as3q4Oubj-WK"
   },
   "source": [
    "## annotate subscriptions with borrow count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ScAqLNhOE5uZ",
    "outputId": "716223f2-39f6-4c2f-9fae-9cbec6dcc86a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      252\n",
       "3      156\n",
       "4      144\n",
       "5      138\n",
       "2      121\n",
       "      ... \n",
       "87       1\n",
       "61       1\n",
       "159      1\n",
       "133      1\n",
       "109      1\n",
       "Name: borrow_count, Length: 83, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# identify subscriptions with at least one overlapping borrow\n",
    "# for each subscription event, count the number of borrow events for the same first member uri with any overlap in start/end dates\n",
    "\n",
    "# make a copy of the dataframe so pandas won't complain about setting values on a slice\n",
    "member_subs_df = member_subs.copy()\n",
    "\n",
    "def borrows_within_sub(sub):\n",
    "  return len(borrow_events_df[\n",
    "        (borrow_events_df.first_member_uri == sub.first_member_uri) &\n",
    "        (borrow_events_df.start_date <= sub.end_date) &\n",
    "        (borrow_events_df.end_date >= sub.start_date)])\n",
    "\n",
    "\n",
    "member_subs_df['borrow_count'] = member_subs_df.apply(borrows_within_sub, axis=1)\n",
    "member_subs_df.borrow_count.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 870
    },
    "id": "dyedD93-i_U0",
    "outputId": "662f4e20-0761-4aa2-f48a-56c421e79008"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-cb49cb58-81b6-472b-a0d2-fd4ba2b7477f\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_type</th>\n",
       "      <th>start_date</th>\n",
       "      <th>end_date</th>\n",
       "      <th>member_uris</th>\n",
       "      <th>member_names</th>\n",
       "      <th>member_sort_names</th>\n",
       "      <th>subscription_price_paid</th>\n",
       "      <th>subscription_deposit</th>\n",
       "      <th>subscription_duration</th>\n",
       "      <th>subscription_duration_days</th>\n",
       "      <th>...</th>\n",
       "      <th>viaf_url</th>\n",
       "      <th>wikipedia_url</th>\n",
       "      <th>nationalities</th>\n",
       "      <th>addresses</th>\n",
       "      <th>postal_codes</th>\n",
       "      <th>arrondissements</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>notes</th>\n",
       "      <th>updated</th>\n",
       "      <th>borrow_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1919-11-22</td>\n",
       "      <td>1920-11-22</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Marie Boscq / Bosq</td>\n",
       "      <td>Boscq, Marie</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1 year</td>\n",
       "      <td>366.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2 rue Crébillon, Paris</td>\n",
       "      <td>75006</td>\n",
       "      <td>6</td>\n",
       "      <td>48.85025, 2.33820</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-26T23:22:24+00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1920-12-31</td>\n",
       "      <td>1921-06-30</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Marie Boscq / Bosq</td>\n",
       "      <td>Boscq, Marie</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6 months</td>\n",
       "      <td>181.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2 rue Crébillon, Paris</td>\n",
       "      <td>75006</td>\n",
       "      <td>6</td>\n",
       "      <td>48.85025, 2.33820</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-26T23:22:24+00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Renewal</td>\n",
       "      <td>1921-08-25</td>\n",
       "      <td>1922-02-25</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Marie Boscq / Bosq</td>\n",
       "      <td>Boscq, Marie</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6 months</td>\n",
       "      <td>184.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2 rue Crébillon, Paris</td>\n",
       "      <td>75006</td>\n",
       "      <td>6</td>\n",
       "      <td>48.85025, 2.33820</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-26T23:22:24+00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1919-11-24</td>\n",
       "      <td>1920-05-24</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Mrs. Bernheim</td>\n",
       "      <td>Bernheim, Mrs.</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6 months</td>\n",
       "      <td>182.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9 rue Alfred de Vigny, Paris</td>\n",
       "      <td>75008</td>\n",
       "      <td>8</td>\n",
       "      <td>48.87941, 2.30488</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-03T00:56:57+00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Renewal</td>\n",
       "      <td>1921-02-18</td>\n",
       "      <td>1921-05-18</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Mrs. Bernheim</td>\n",
       "      <td>Bernheim, Mrs.</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3 months</td>\n",
       "      <td>89.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9 rue Alfred de Vigny, Paris</td>\n",
       "      <td>75008</td>\n",
       "      <td>8</td>\n",
       "      <td>48.87941, 2.30488</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-03T00:56:57+00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8532</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1940-02-02</td>\n",
       "      <td>1940-03-02</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Mrs. Quennell</td>\n",
       "      <td>Quennell, Mrs.</td>\n",
       "      <td>45.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1 month</td>\n",
       "      <td>29.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51 avenue Marceau, Paris</td>\n",
       "      <td>75116</td>\n",
       "      <td>16</td>\n",
       "      <td>48.86931, 2.29803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-03T20:29:50+00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8701</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1940-09-30</td>\n",
       "      <td>1940-10-30</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Simone Abreu / Mme Simone de Bretteville</td>\n",
       "      <td>de Bretteville, Simone</td>\n",
       "      <td>25.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1 month</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>87 avenue de Wagram, Paris</td>\n",
       "      <td>75017</td>\n",
       "      <td>17</td>\n",
       "      <td>48.88095, 2.29972</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-12T17:04:54+00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8968</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1941-12-02</td>\n",
       "      <td>1942-01-02</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Odile Brull</td>\n",
       "      <td>Brull, Odile</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1 month</td>\n",
       "      <td>31.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1 rue Neuve, Versailles</td>\n",
       "      <td>78000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.80891, 2.12545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-11-11T20:05:08+00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9051</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1941-10-30</td>\n",
       "      <td>1941-11-30</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Jean Roy</td>\n",
       "      <td>Roy, Jean</td>\n",
       "      <td>20.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1 month</td>\n",
       "      <td>31.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102 rue Charles Laffitte, Neuilly-sur-Seine</td>\n",
       "      <td>92200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.88063, 2.26340</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31T03:37:09+00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9074</th>\n",
       "      <td>Subscription</td>\n",
       "      <td>1941-11-28</td>\n",
       "      <td>1941-12-28</td>\n",
       "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
       "      <td>Bernard Lestocquoy</td>\n",
       "      <td>Lestocquoy, Bernard</td>\n",
       "      <td>25.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1 month</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
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      "text/plain": [
       "        event_type  start_date    end_date  \\\n",
       "30    Subscription  1919-11-22  1920-11-22   \n",
       "31    Subscription  1920-12-31  1921-06-30   \n",
       "32         Renewal  1921-08-25  1922-02-25   \n",
       "46    Subscription  1919-11-24  1920-05-24   \n",
       "47         Renewal  1921-02-18  1921-05-18   \n",
       "...            ...         ...         ...   \n",
       "8532  Subscription  1940-02-02  1940-03-02   \n",
       "8701  Subscription  1940-09-30  1940-10-30   \n",
       "8968  Subscription  1941-12-02  1942-01-02   \n",
       "9051  Subscription  1941-10-30  1941-11-30   \n",
       "9074  Subscription  1941-11-28  1941-12-28   \n",
       "\n",
       "                                            member_uris  \\\n",
       "30    https://shakespeareandco.princeton.edu/members...   \n",
       "31    https://shakespeareandco.princeton.edu/members...   \n",
       "32    https://shakespeareandco.princeton.edu/members...   \n",
       "46    https://shakespeareandco.princeton.edu/members...   \n",
       "47    https://shakespeareandco.princeton.edu/members...   \n",
       "...                                                 ...   \n",
       "8532  https://shakespeareandco.princeton.edu/members...   \n",
       "8701  https://shakespeareandco.princeton.edu/members...   \n",
       "8968  https://shakespeareandco.princeton.edu/members...   \n",
       "9051  https://shakespeareandco.princeton.edu/members...   \n",
       "9074  https://shakespeareandco.princeton.edu/members...   \n",
       "\n",
       "                                  member_names       member_sort_names  \\\n",
       "30                          Marie Boscq / Bosq            Boscq, Marie   \n",
       "31                          Marie Boscq / Bosq            Boscq, Marie   \n",
       "32                          Marie Boscq / Bosq            Boscq, Marie   \n",
       "46                               Mrs. Bernheim          Bernheim, Mrs.   \n",
       "47                               Mrs. Bernheim          Bernheim, Mrs.   \n",
       "...                                        ...                     ...   \n",
       "8532                             Mrs. Quennell          Quennell, Mrs.   \n",
       "8701  Simone Abreu / Mme Simone de Bretteville  de Bretteville, Simone   \n",
       "8968                               Odile Brull            Brull, Odile   \n",
       "9051                                  Jean Roy               Roy, Jean   \n",
       "9074                        Bernard Lestocquoy     Lestocquoy, Bernard   \n",
       "\n",
       "      subscription_price_paid  subscription_deposit subscription_duration  \\\n",
       "30                       28.0                   7.0                1 year   \n",
       "31                       28.0                   NaN              6 months   \n",
       "32                       35.0                   NaN              6 months   \n",
       "46                       16.0                   NaN              6 months   \n",
       "47                       16.0                   NaN              3 months   \n",
       "...                       ...                   ...                   ...   \n",
       "8532                     45.0                  50.0               1 month   \n",
       "8701                     25.0                  50.0               1 month   \n",
       "8968                     20.0                   NaN               1 month   \n",
       "9051                     20.0                  50.0               1 month   \n",
       "9074                     25.0                  50.0               1 month   \n",
       "\n",
       "      subscription_duration_days  ...  viaf_url wikipedia_url nationalities  \\\n",
       "30                         366.0  ...       NaN           NaN           NaN   \n",
       "31                         181.0  ...       NaN           NaN           NaN   \n",
       "32                         184.0  ...       NaN           NaN           NaN   \n",
       "46                         182.0  ...       NaN           NaN           NaN   \n",
       "47                          89.0  ...       NaN           NaN           NaN   \n",
       "...                          ...  ...       ...           ...           ...   \n",
       "8532                        29.0  ...       NaN           NaN           NaN   \n",
       "8701                        30.0  ...       NaN           NaN        France   \n",
       "8968                        31.0  ...       NaN           NaN           NaN   \n",
       "9051                        31.0  ...       NaN           NaN           NaN   \n",
       "9074                        30.0  ...       NaN           NaN           NaN   \n",
       "\n",
       "                                        addresses postal_codes  \\\n",
       "30                         2 rue Crébillon, Paris        75006   \n",
       "31                         2 rue Crébillon, Paris        75006   \n",
       "32                         2 rue Crébillon, Paris        75006   \n",
       "46                   9 rue Alfred de Vigny, Paris        75008   \n",
       "47                   9 rue Alfred de Vigny, Paris        75008   \n",
       "...                                           ...          ...   \n",
       "8532                     51 avenue Marceau, Paris        75116   \n",
       "8701                   87 avenue de Wagram, Paris        75017   \n",
       "8968                      1 rue Neuve, Versailles        78000   \n",
       "9051  102 rue Charles Laffitte, Neuilly-sur-Seine        92200   \n",
       "9074                      80 rue Lauriston, Paris        75116   \n",
       "\n",
       "      arrondissements        coordinates notes                    updated  \\\n",
       "30                  6  48.85025, 2.33820   NaN  2021-07-26T23:22:24+00:00   \n",
       "31                  6  48.85025, 2.33820   NaN  2021-07-26T23:22:24+00:00   \n",
       "32                  6  48.85025, 2.33820   NaN  2021-07-26T23:22:24+00:00   \n",
       "46                  8  48.87941, 2.30488   NaN  2020-01-03T00:56:57+00:00   \n",
       "47                  8  48.87941, 2.30488   NaN  2020-01-03T00:56:57+00:00   \n",
       "...               ...                ...   ...                        ...   \n",
       "8532               16  48.86931, 2.29803   NaN  2020-01-03T20:29:50+00:00   \n",
       "8701               17  48.88095, 2.29972   NaN  2021-07-12T17:04:54+00:00   \n",
       "8968              NaN  48.80891, 2.12545   NaN  2020-11-11T20:05:08+00:00   \n",
       "9051              NaN  48.88063, 2.26340   NaN  2019-12-31T03:37:09+00:00   \n",
       "9074               16  48.86765, 2.28811   NaN  2019-11-19T14:47:34+00:00   \n",
       "\n",
       "     borrow_count  \n",
       "30              0  \n",
       "31              0  \n",
       "32              0  \n",
       "46              0  \n",
       "47              0  \n",
       "...           ...  \n",
       "8532            0  \n",
       "8701            0  \n",
       "8968            0  \n",
       "9051            0  \n",
       "9074            0  \n",
       "\n",
       "[252 rows x 52 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subscriptions with no borrows\n",
    "member_subs_df[member_subs_df.borrow_count == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ns6eEi33jJi5"
   },
   "source": [
    "Preliminary inspection of subscriptions with no borrows — calculation looks reasonable\n",
    "\n",
    "- Marie Boscq: subs in 1920-21 but borrowing not documented until 1932\n",
    "- Mrs. Berheim: similar: no borrowing records for early subscriptiosn (1920s)\n",
    "- Mrs. Quennell: 1m sub in 1940; 1 borrow in 1940 with no end date "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TvwUzyhFgAZc"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4F6LZoa1kOs8"
   },
   "source": [
    "## calculate daily books out vs subscription volumes for overlapping borrows/subscriptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "nABpJsqLkNsQ",
    "outputId": "54fc18d1-fcd7-457b-d367-179298b89f53"
   },
   "outputs": [
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       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "           date  books_out  subscription_volumes\n",
       "0    1919-11-17          0                   1.0\n",
       "1    1919-11-18          3                   4.0\n",
       "2    1919-11-19          5                   5.0\n",
       "3    1919-11-20          5                   5.0\n",
       "4    1919-11-21          5                   5.0\n",
       "...         ...        ...                   ...\n",
       "8321 1942-08-29          2                   1.0\n",
       "8322 1942-08-30          2                   1.0\n",
       "8323 1942-08-31          2                   1.0\n",
       "8324 1942-09-01          2                   1.0\n",
       "8325 1942-09-02          2                   1.0\n",
       "\n",
       "[8326 rows x 3 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# repeat the same plot but limited to borrows and subscriptions that overlap\n",
    "\n",
    "# subscription events with at least one borrow\n",
    "subs_with_borrows = member_subs_df[member_subs_df.borrow_count != 0] \n",
    "# borrows that overlap with a subscription\n",
    "borrows_within_subs = borrow_events_df[borrow_events_df.within_subscription != 0]\n",
    "\n",
    "# adapt previous code rerun with the filtered events \n",
    "# go through end of last subscription end date\n",
    "one_day = timedelta(days=1)\n",
    "# start with the earliest start date\n",
    "day = date_events.start_datetime.iloc[0]\n",
    "\n",
    "overlap_dates = []\n",
    "overlap_books_out = []\n",
    "overlap_subscription_vols = []\n",
    "\n",
    "while day < subs_end_date:\n",
    "     overlap_dates.append(day)\n",
    "     # get total number of borrowing events (within subscriptions) spanning this date; include start date, exclude end (to avoid double counting)\n",
    "     overlap_books_out.append(len(borrows_within_subs[(borrows_within_subs.start_datetime <= day) & (borrows_within_subs.end_datetime > day)]))\n",
    "     # total number of volumes from active subscriptions (with borrows) for this date\n",
    "     overlap_subscription_vols.append(subs_with_borrows[(subs_with_borrows.start_datetime <= day) & (day < subs_with_borrows.end_datetime)].subscription_volumes.sum())\n",
    "\n",
    "     day += one_day\n",
    "\n",
    "overlap_borrowing_df  = pd.DataFrame()\n",
    "overlap_borrowing_df['date'] = overlap_dates\n",
    "overlap_borrowing_df['books_out'] = overlap_books_out\n",
    "overlap_borrowing_df['subscription_volumes'] = overlap_subscription_vols\n",
    "overlap_borrowing_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U8Ni0Vr9vj-C"
   },
   "source": [
    "### chart: borrows vs subscription volumes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "id": "qk_BYyBDk354",
    "outputId": "17c6ecc8-cc34-47ec-df41-d7c1a0a4af34"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "overlap_borrowing_df.plot(kind='line',x='date',y='books_out',ax=ax, figsize=(25,7),\n",
    "                   title='books out vs. subscription volumes for overlapping borrows/subscriptions')\n",
    "overlap_borrowing_df.plot(kind='line',x='date',y='subscription_volumes', color='green', ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YP7r0ldOyIES"
   },
   "source": [
    "In this version of the graph, the books out and subscription volumes track each other much more closely through the early 1930s. Still not sure what is going on after 1935! I thought limiting to overlapping subscriptions and borrows would make this clearer. Are there data errors? Did I not limit things the way I thought? Did Beach get more lax in enforcing subscription limits? Could including long checkouts be skewing the results?\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "QJMf00_xyG5X",
    "outputId": "9b653353-7480-4ffc-9342-6aa39882c66c"
   },
   "outputs": [
    {
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      "text/plain": [
       "        date  books_out  subscription_volumes\n",
       "0 1919-11-17          0                   1.0\n",
       "1 1919-11-18          3                   4.0\n",
       "2 1919-11-19          5                   5.0\n",
       "3 1919-11-20          5                   5.0\n",
       "4 1919-11-21          5                   5.0"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overlap_borrowing_df.head()"
   ]
  },
  {
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   "execution_count": null,
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     "height": 361
    },
    "id": "dbizww4jyLA4",
    "outputId": "2b896c7b-e1d4-4630-bf2d-65cdd6299b77"
   },
   "outputs": [
    {
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\"total\": 5.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 10.0}, {\"date\": \"1942-05-05T00:00:00\", \"total\": 5.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 10.0}, {\"date\": \"1942-05-06T00:00:00\", \"total\": 4.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 10.0}, {\"date\": \"1942-05-07T00:00:00\", \"total\": 4.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 10.0}, {\"date\": \"1942-05-08T00:00:00\", \"total\": 4.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 10.0}, {\"date\": \"1942-05-09T00:00:00\", \"total\": 4.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 10.0}, {\"date\": \"1942-05-10T00:00:00\", \"total\": 4.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 10.0}, {\"date\": \"1942-05-11T00:00:00\", \"total\": 4.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 10.0}, {\"date\": \"1942-05-12T00:00:00\", \"total\": 4.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 10.0}, {\"date\": \"1942-05-13T00:00:00\", \"total\": 4.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 10.0}, {\"date\": \"1942-05-14T00:00:00\", \"total\": 4.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 10.0}, {\"date\": \"1942-05-15T00:00:00\", \"total\": 4.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 10.0}, {\"date\": \"1942-05-16T00:00:00\", \"total\": 4.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 10.0}, {\"date\": \"1942-05-17T00:00:00\", \"total\": 4.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 10.0}, {\"date\": \"1942-05-18T00:00:00\", \"total\": 4.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 10.0}, {\"date\": \"1942-05-19T00:00:00\", \"total\": 4.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 10.0}, {\"date\": \"1942-05-20T00:00:00\", \"total\": 4.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 10.0}, {\"date\": \"1942-05-21T00:00:00\", \"total\": 4.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 10.0}, {\"date\": \"1942-05-22T00:00:00\", \"total\": 4.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 8.0}, {\"date\": \"1942-05-23T00:00:00\", \"total\": 4.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 8.0}, {\"date\": \"1942-05-24T00:00:00\", \"total\": 4.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 8.0}, {\"date\": \"1942-05-25T00:00:00\", \"total\": 4.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 8.0}, {\"date\": \"1942-05-26T00:00:00\", \"total\": 4.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-05-27T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-05-28T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-05-29T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-05-30T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-05-31T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-01T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-02T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-03T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-04T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-05T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-06T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-07T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-08T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-09T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-10T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-11T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-12T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-13T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-14T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-15T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-16T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-17T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-18T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-19T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-20T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-21T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-22T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-23T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-24T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 4.0}, {\"date\": \"1942-06-25T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 3.0}, {\"date\": \"1942-06-26T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 3.0}, {\"date\": \"1942-06-27T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 3.0}, {\"date\": \"1942-06-28T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 3.0}, {\"date\": \"1942-06-29T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 3.0}, {\"date\": \"1942-06-30T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 3.0}, {\"date\": \"1942-07-01T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 3.0}, {\"date\": \"1942-07-02T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 3.0}, {\"date\": \"1942-07-03T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 3.0}, {\"date\": \"1942-07-04T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 3.0}, {\"date\": \"1942-07-05T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 3.0}, {\"date\": \"1942-07-06T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 3.0}, {\"date\": \"1942-07-07T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 3.0}, {\"date\": \"1942-07-08T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 3.0}, {\"date\": \"1942-07-09T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 3.0}, {\"date\": \"1942-07-10T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 3.0}, {\"date\": \"1942-07-11T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 3.0}, {\"date\": \"1942-07-12T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 3.0}, {\"date\": \"1942-07-13T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 3.0}, {\"date\": \"1942-07-14T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 3.0}, {\"date\": \"1942-07-15T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 3.0}, {\"date\": \"1942-07-16T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 3.0}, {\"date\": \"1942-07-17T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-07-18T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-07-19T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-07-20T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-07-21T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-07-22T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-07-23T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-07-24T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-07-25T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-07-26T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-07-27T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-07-28T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-07-29T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-07-30T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-07-31T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-01T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-02T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-03T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-04T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-05T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-06T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-07T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-08T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-09T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-10T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-11T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-12T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-13T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-14T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-15T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-16T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-17T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-18T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-19T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-20T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-21T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-22T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-23T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-24T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-25T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-26T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-27T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-28T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-29T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-30T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-08-31T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-09-01T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}, {\"date\": \"1942-09-02T00:00:00\", \"total\": 1.0, \"subscription_volumes\": null, \"type\": \"subscription volumes\", \"books_out\": 2.0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# consolidate into the structure altair wants for multiline charts, to simplify graphing with altair\n",
    "overlap_books_out = overlap_borrowing_df.rename(columns={\"date\": \"date\", \"books_out\": \"total\"})\n",
    "overlap_books_out['type'] = \"books out\"\n",
    "\n",
    "overlap_subs_vols = overlap_borrowing_df.rename(columns={\"date\": \"date\", \"subscription_volumes\": \"total\"})\n",
    "overlap_subs_vols['type'] = \"subscription volumes\"\n",
    "\n",
    "\n",
    "alt_overlap_borrowing = pd.concat([overlap_books_out, overlap_subs_vols])\n",
    "\n",
    "\n",
    "color_domain = ['books out', 'subscription volumes']\n",
    "color_range = ['#5276A7', 'green']\n",
    "\n",
    "\n",
    "alt.Chart(alt_overlap_borrowing).mark_line(strokeWidth=0.75).encode(\n",
    "    x='date',\n",
    "    y='total',\n",
    "    color=alt.Color('type', scale=alt.Scale(domain=color_domain, range=color_range), title=\"\")\n",
    ").properties(\n",
    "    width=1200,\n",
    "    height=chart_height,\n",
    "    title='books out vs. subscription volumes for overlapping borrows/subscriptions'\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_9GKd8ddw2e0"
   },
   "source": [
    "## percent of available subscription volumes used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "Obqo2-dsgA9f",
    "outputId": "8b1dea5f-4b8d-4f65-f6ce-1ddbaad72fb8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-5593d428-999c-428f-b8db-f46193595928\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>books_out</th>\n",
       "      <th>subscription_volumes</th>\n",
       "      <th>pct_used</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1919-11-17</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1919-11-18</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1919-11-19</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1919-11-20</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1919-11-21</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8321</th>\n",
       "      <td>1942-08-29</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8322</th>\n",
       "      <td>1942-08-30</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8323</th>\n",
       "      <td>1942-08-31</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8324</th>\n",
       "      <td>1942-09-01</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8325</th>\n",
       "      <td>1942-09-02</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8326 rows × 4 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5593d428-999c-428f-b8db-f46193595928')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-5593d428-999c-428f-b8db-f46193595928 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-5593d428-999c-428f-b8db-f46193595928');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "           date  books_out  subscription_volumes  pct_used\n",
       "0    1919-11-17          0                   1.0      0.00\n",
       "1    1919-11-18          3                   4.0      0.75\n",
       "2    1919-11-19          5                   5.0      1.00\n",
       "3    1919-11-20          5                   5.0      1.00\n",
       "4    1919-11-21          5                   5.0      1.00\n",
       "...         ...        ...                   ...       ...\n",
       "8321 1942-08-29          2                   1.0      2.00\n",
       "8322 1942-08-30          2                   1.0      2.00\n",
       "8323 1942-08-31          2                   1.0      2.00\n",
       "8324 1942-09-01          2                   1.0      2.00\n",
       "8325 1942-09-02          2                   1.0      2.00\n",
       "\n",
       "[8326 rows x 4 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate what percent of available subscription volumes was used\n",
    "overlap_borrowing_df['pct_used'] = overlap_borrowing_df.apply(lambda x: x.books_out / x.subscription_volumes, axis=1)\n",
    "overlap_borrowing_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-hMkNyacwv7O",
    "outputId": "c6345b88-49cd-4340-a588-28d1129b4807"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "min     0.0000\n",
       "max     4.5000\n",
       "mean    1.3994\n",
       "Name: pct_used, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overlap_borrowing_df.pct_used.agg(['min', 'max', 'mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 284
    },
    "id": "DHABDfUJgyBg",
    "outputId": "44b007d0-3378-4fc1-ceec-5cfc5cae0e6c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f13bed6b2b0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "overlap_borrowing_df.pct_used.hist(bins=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7Gd2Tn680gu2"
   },
   "source": [
    "# preliminary conclusion\n",
    "\n",
    "Using the known subscription volumes as a **minimum** for likely book activity seems reasonable. Available evidence suggests there was _more_ borrowing going on than that, at least in the later years."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "include_colab_link": true,
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
